A Survey on Blockchain-Based Federated Learning

Abstract

Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. In recent years, there have been notable advancements in combining these two technologies, particularly in data privacy protection, data sharing incentives, and computational performance. Although there are some surveys on blockchain-based federated learning (BFL), these surveys predominantly focus on the BFL framework and its classifications, yet lack in-depth analyses of the pivotal issues addressed by BFL. This work aims to assist researchers in understanding the latest research achievements and development directions in the integration of FL with blockchains. Firstly, we introduced the relevant research in FL and blockchain technology and highlighted the existing shortcomings of FL. Next, we conducted a comparative analysis of existing BFL frameworks, delving into the significant problems in the realm of FL that the combination of blockchain and FL addresses. Finally, we summarized the application prospects of BFL technology in various domains such as the Internet of Things, Industrial Internet of Things, Internet of Vehicles, and healthcare services, as well as the challenges that need to be addressed and future research directions.

Full text

Citation: Wu, L.; Ruan, W.; Hu, J.; He,
Y. A Survey on Blockchain-Based
Federated Learning. Future Internet
2023,15, 400. https://doi.org/
10.3390/fi15120400
Academic Editor: Giovanni Pau
Received: 8 November 2023
Revised: 30 November 2023
Accepted: 1 December 2023
Published: 12 December 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
future internet
Review
A Survey on Blockchain-Based Federated Learning
Lang Wu, Weijian Ruan *, Jinhui Hu and Yaobin He
China Electronics Technology Group Corporation (CETC), Key Laboratory of Smart City Model Simulation and
Intelligent Technology, The Smart City Research Institute of CETC and National Center for Applied Mathematics
Shenzhen (NCAMS), Shenzhen 518038, China; wulang@cetc.com.cn (L.W.); hujinhui@cetc.com.cn (J.H.);
heyaobin@cetc.com.cn (Y.H.)
*Correspondence: ruanweijian@cetc.com.cn
Abstract:
Federated learning (FL) and blockchains exhibit significant commonality, complementarity,
and alignment in various aspects, such as application domains, architectural features, and privacy
protection mechanisms. In recent years, there have been notable advancements in combining these
two technologies, particularly in data privacy protection, data sharing incentives, and computational
performance. Although there are some surveys on blockchain-based federated learning (BFL), these
surveys predominantly focus on the BFL framework and its classifications, yet lack in-depth analyses
of the pivotal issues addressed by BFL. This work aims to assist researchers in understanding the
latest research achievements and development directions in the integration of FL with blockchains.
Firstly, we introduced the relevant research in FL and blockchain technology and highlighted the
existing shortcomings of FL. Next, we conducted a comparative analysis of existing BFL frameworks,
delving into the significant problems in the realm of FL that the combination of blockchain and FL
addresses. Finally, we summarized the application prospects of BFL technology in various domains
such as the Internet of Things, Industrial Internet of Things, Internet of Vehicles, and healthcare
services, as well as the challenges that need to be addressed and future research directions.
Keywords: blockchain; federated learning; security and privacy; Internet of Things
1. Introduction
Currently, artificial intelligence (AI) technology is advancing rapidly, transitioning
from its invention phase a few years ago to the phase of practical application. AI technology
is being applied in an increasing number of scenarios. However, as algorithms and com-
puting power have significantly improved, there is a growing demand for larger datasets
and increased emphasis on data privacy protection. How to meet the data requirements
of AI models in this context has become an urgent challenge in the development of AI
technology today.
Presently, machine learning (ML) algorithms increasingly rely on vast amounts of data.
However, the reality is that, due to privacy constraints, data are scattered across different
organizations. Therefore, the current development of AI faces two challenges: the data
silo problem and issues related to data security and privacy. Firstly, the data silo problem
greatly limits the availability of big data. Despite the internet generating billions of data
daily, there is a lack of useful, high-dimensional, high-quality data. Secondly, countries
worldwide are strengthening their data security protection and privacy. Strict regulations
on user data privacy and security management are becoming a global trend. Without
providing users with reliable privacy protection methods, the issue of data insufficiency
will severely restrict the development of AI.
Due to these factors, the emerging ML technique, federated learning (FL) [
1
,
2
], has
become a popular research topic in the field of ML. The main idea of FL is to enable a
large number of user devices that store data locally (referred to as clients) to collaboratively
train a single ML model without the need to share their raw data. For example, data
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Future Internet 2023,15, 400 2 of 22
from different hospitals are often isolated, creating data silos. Because each data silo has
limitations in terms of data size and approximation to the actual distribution, a single
hospital may struggle to train and attain high-quality models with high prediction accuracy
for specific tasks. Ideally, if multiple hospitals could collaborate and combine their data to
train ML models collectively, more accurate training results could be achieved. However,
due to various policies and regulations, data cannot be easily shared between hospitals.
Similarly, the data silo phenomenon is prevalent in many other fields, including finance,
government, and supply chains. Additionally, policies like the general data protection
regulation (GDPR) [
3
] set rules for data sharing between different organizations. As a result,
developing an FL method that can deliver excellent prediction accuracy while adhering to
policies and regulations to protect privacy is a highly challenging endeavor.
In addition, FL also has its unique set of challenges. Primarily, the paradigm often
necessitates the involvement of a considerable number of users with diverse cultural back-
grounds and intricate behavioral patterns, complicating mutual trust and augmenting
the risk of inadvertent privacy breaches for honest participants [
4
–
8
]. FL protects users’
sensitive data by keeping the source data local and only exchanging model updates, such as
gradient information. However, research indicates that gradient information can leak users’
private data [
9
–
15
]. Attackers can indirectly infer label information and dataset member-
ship information through the gradient information uploaded by clients.
Carlini et al. [13]
extracted sensitive user data, such as specific bank card numbers, from a recursive neural
network trained on users’ language data. Fredrikson et al. [
10
] investigated how to steal
data privacy from model information and conducted inversion attacks on linear regression
models through dosage prediction experiments, obtaining sensitive patient information.
Hitaj et al. [
12
] launched attacks on model aggregation using generative adversarial net-
works (GANs). The experimental results show that malicious clients can steal users’ data
privacy by generating similar local model updates. Gei et al. [
15
] demonstrated the fea-
sibility of reconstructing input data from gradient information, independent of the deep
network architecture, and recovered a batch of input images using cosine similarity and
adversarial attack methods. Secondly, the attainment of a global model in FL involves
multiple iterative rounds of model updates from users, engendering significant communi-
cation overhead and incurring additional storage costs during network transmission [
16
,
17
].
Moreover, the distinction between federated learning and distributed computing lies in
the fact that the dataset in FL comes from various end-user terminals, and the features of
data generated by these users are often non-independent and non-identically distributed
(non-IID). Traditional distributed framework algorithms perform well only when dealing
with independent and identically distributed (IID) data, while they encounter challenges
such as difficulty in convergence and excessive communication rounds when handling
non-IID data [
18
]. Thirdly, the integrity of the global model may be compromised due to
malevolent participants or a central server susceptible to cyber-attacks [
19
]. Lastly, the local
devices involved could themselves be malicious or vulnerable to exploitation, potentially
resulting in the unauthorized disclosure or manipulation of transmitted information [20].
In recent years, blockchain technology, originating from Bitcoin, has undergone
rapid advancement [
21
]. Built upon a decentralized peer-to-peer network architecture,
blockchains ensure that transactional data are stored across all network nodes, while its
immutability and consistency are guaranteed by consensus algorithms. Innovatively estab-
lishing decentralized trust, blockchain technology allows individuals to opt for believing in
the reliability of cryptographic algorithms and the honesty of the majority of nodes within
the peer-to-peer network, rather than being compelled to place trust in a single entity [
22
].
This mechanism of decentralized trust offers a new avenue for augmenting the capabilities
of FL. For instance, FL can not only leverage the consistency provided by blockchain’s con-
sensus mechanisms to establish trustworthy interactions within an untrusted environment
but can also utilize the economic property derived from blockchain’s incentive schemes
to effectively promote information sharing within the federated ecosystem. Through the
accumulated technical advancements in FL and blockchains over the years, as well as their

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exploration and applications in various relevant fields, blockchain-based federated learning
(BFL) has gained the capability and prospects for applications in highly privacy-sensitive
industries. Due to the advantages of blockchain in areas such as identity verification, de-
centralization, traceability, and immutability, many research efforts have used blockchains
as underlying structures for FL. They achieve distributed model aggregation tasks by de-
signing protocols on top of the blockchain. While blockchain is an effective way to replace
the central server in FL and enhances security in the storage and update processes of FL
models, it also introduces new challenges in FL application scenarios, such as training
efficiency, resource allocation, and communication delays.
At present, limited literature exists that explores the integration of blockchain and
FL. Toyoda et al. [
23
] introduced the categories and platforms of blockchain technologies
employed in existing BFL research work, and made comparisons between various BFL
frameworks. Hou et al. [
24
] compared and summarized some prevailing BFL frameworks,
underlying BFL infrastructures, and applications of BFL. Wahab et al. [
25
] engaged in a
comprehensive survey targeting FL, wherein the comparative analysis spanned aspects
such as architectural paradigms, communication efficiency, incentive mechanisms, privacy
preservation, and secure aggregation schemes, and also incorporated an investigation
of certain BFL architectures. Nguyen et al. [
26
] explored the integration of blockchain
and FL, taking into account communication costs and resource allocation in mobile edge
computing scenarios. Issa et al. [
27
] delved into this topic within the context of the Internet
of Things. They provided detailed discussions on both blockchains and FL separately,
and presented structures and perspectives on their integration. Li et al. [
28
] examined
the architecture of BFL, covering aspects such as types, design, model enhancement, and
incentive mechanisms.
It is evident that these surveys predominantly focus on the BFL framework and its
prospective applications in the field of AI, yet lack an in-depth analysis of the pivotal
issues addressed by BFL, as well as a comprehensive discussion on its applicability in more
expansive scenarios. Therefore, this work originates from the framework of BFL, providing
an incisive discourse on key challenges in FL that are ameliorated through the integration
of blockchains. It further elaborates on the prospective applications of BFL in multiple
domains, including the Internet of Things (IoT), Industrial Internet of Things (IIoT), Internet
of Vehicles (IoV), and healthcare services. The paper undertakes a holistic and rigorous
analysis and comparative evaluation across three critical dimensions—fundamental archi-
tecture, core technologies, and future applications—to ultimately summarize the innovative
directions and applicative frontiers where blockchains and FL converge.
The main contributions of this work are as follows:
•
We offer an overview encompassing the definition, architectural design, and challenges
of both blockchains and FL. We also delve into the motivations driving the application
of blockchains in the context of FL.
•
We categorize BFL frameworks into three distinct classes based on how blockchains
participated in the FL process within individual nodes.
•
We elaborate on how to use blockchain technology to mitigate the challenges of FL
from the perspectives of decentralization, incentive mechanisms, attack resistance,
privacy protection, and efficiency enhancement.
•
We compile a comprehensive list of current viable applications for BFL and engage in
discussions regarding the promising future directions and unresolved issues in the
field of BFL.
The rest of this article is organized as follows. In Section 2, we introduce the basics of
FL and blockchains, and we present the frameworks and functions of BFL in Section 3. In
Section 4, we investigate the applications of BFL in different domains. Discussions of the
current challenges and future research directions of BFL are presented in Section 5, and we
conclude the paper in Section 6.

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2. Preliminary
2.1. Overview of Federated Learning
Conventional ML algorithms necessitate the centralization of raw data on high-
computational-capacity cloud servers for model training, thereby engendering uncontrol-
lable data flow and vulnerability to sensitive data leakage. Mcmahan et al. [
17
] introduced
the concept of FL in 2017, allowing for the preservation of user privacy during the ML
process without the aggregation of source data into a shared training dataset. Essentially,
FL is a form of distributed machine learning technology, the workflow of which is depicted
in Figure 1.
Figure 1. The workflow of FL.
Client devices, such as mobile phones, computers, and IoT devices, work together to
train ML models under the supervision of a central server. In this configuration, the client
devices are in charge of training local data to build local models. The central server performs
weighted aggregation of these local models to produce a global model. Through iterative
cycles of this process, a model wis ultimately obtained that closely approximates the
outcomes of centralized ML algorithms, thereby effectively mitigating numerous privacy
risks associated with the aggregation of source data in traditional ML paradigms.
The iterative process of FL is outlined below:
1. Client devices retrieve the global parameter wt−1from the server;
2.
Each client ktrains its local data to derive its local model
wt,k
(signifying the local
model update for the kth client in the tth communication round);
3. All participating clients transmit their local model updates to the central server;
4.
Upon receiving updates from diverse clients, the central server executes weighted
aggregation operations to formulate the global model
wt
(indicating the global model
update in the tth communication round).
Foremost, FL technology exhibits the following distinctive attributes: (1) The raw
data engaged in FL are retained locally on client devices, with only model updates being
exchanged with the central server. (2) The jointly trained model is shared collectively
among all participating entities. (3) The ultimate model accuracy of FL approximates
that of centralized machine learning methodologies. (4) The quality of the training data
contributed by participants in FL is directly correlated with the precision of the resultant
global model.

Future Internet 2023,15, 400 5 of 22
2.2. Threats and Challenges of FL
Since the concept of FL was proposed, it has quickly attracted widespread atten-
tion and research in the academic community. However, there are still many threats
and challenges that urgently need to be addressed in this research direction. The most
core issues include single point of failure [
29
–
31
], lack of incentive mechanisms [
20
,
32
],
poisoning attacks [
33
–
37
], defects in privacy policies [
9
–
12
,
14
] and low communication
efficiency [16,17,38]
. These issues have greatly limited the further development and appli-
cation of FL.
Single Point of Failure
: The central server in FL is susceptible to malicious updates,
causing defects in the global model update. This affects all local model updates and reduces
their accuracy. Additionally, FL requires local devices to upload local model updates to the
central server. When too many devices are transmitting models simultaneously, it can lead
to network overload.
Lack of Incentive Mechanism
: FL generally assumes that each local device willingly
contributes data resources to the global model. However, this does not align with reality.
The lack of an incentive mechanism affects participants’ motivation to contribute, and
some participants may even obtain rewards without contributing data, leading to unfair
economic compensation.
Poisoning Attacks
: Malicious users may deliberately upload carefully calculated
malicious local training models to affect global model training, intentionally sabotaging
predictive outcomes of machine learning. This is mainly because FL lacks the ability to
monitor and diagnose malicious users or malicious model updates.
Defects in Privacy Policies
: Despite training data resources being stored on local
devices, the FL framework may still lead to a leakage of training data privacy. In a real
network environment, it is challenging to assess the motivations of participating clients
in the training process, and ensuring the trustworthiness of the central server is equally
difficult. Relying solely on model updates to protect user privacy appears to be insufficient.
Low Communication Efficiency
: Since FL requires communication between clients
and servers to transmit local learning models and perform multiple rounds of model
training iterations for local or global model updates, the communication efficiency between
the client and server, as well as the model training efficiency, can also affect FL performance.
2.3. Overview of Blockchains
Blockchains, initially introduced as part of a payment system known as Bitcoin by
Nakamoto in 2008 [
21
], has become one of the most widely adopted disruptive technologies
in various financial and industrial applications. It is essentially a distributed and immutable
ledger consisting of blocks that are shared among untrusted participants within a peer-to-
peer (P2P) network, eliminating the need for a trusted central authority [
39
]. To ensure the
validity of all transactions before they are recorded, consensus algorithms are employed.
As illustrated in Figure 2each block in the chain contains a hash of the preceding block,
ensuring the immutability of the blocks [
40
]. To maintain data integrity, all network
participants maintain identical copies of the ledger. When a new transaction is generated,
it is disseminated to specific nodes within the network, often referred to as miners. These
miners validate the incoming transaction by verifying its associated signature. Upon
validation, they proceed to create a new block and distribute it across the network, reaching
a consensus through a distributed process. Once the miners reach a consensus and validate
the new block, it is appended to the distributed ledger. From a structural perspective, a
block consists of two parts: the block header and the block body. Key information in the
block header includes the current version number, the hash value of the previous block, a
timestamp, a random number (Nonce), and the hash value of the Merkle Root [41].

Future Internet 2023,15, 400 6 of 22
Figure 2. Structure of a blockchain.
Blockchains can be categorized into four main types: public blockchains [
42
–
44
],
consortium blockchains [
45
–
47
], and private blockchains [
48
–
51
]. A public blockchain
is a system where anyone in the network can access the blockchain at any time. It is
usually considered fully decentralized and highly anonymous, and the data are immutable.
Consortium blockchains are managed collectively by a number of enterprises or institutions.
Data are recorded and maintained by verified participants, and these nodes have the
permission to read the data. A private blockchain is a blockchain controlled by a particular
organization or user. The rules for controlling the number of participating nodes are strict,
resulting in very fast transaction speeds and a higher level of privacy. It is less susceptible
to attacks, and while it offers higher security compared to public blockchains, its degree of
decentralization is significantly reduced.
2.4. Architecture of Blockchains
Blockchain technology has undergone more than a decade of development. Although
there is currently no standardized development form, we can still categorize blockchains
into six layers based on the commonalities of the working modes of existing blockchain
platforms: data layer, network layer, consensus layer, incentive layer, contract layer, and
application layer [22], as shown in Figure 3.
Figure 3. Systematic blockchain architecture.

Future Internet 2023,15, 400 7 of 22
A. Data layer
The data layer is the bottom-most layer of the blockchain platform. This layer primarily
uses data structures such as Merkle trees to organize and manage data within the blockchain,
and employs hash functions and asymmetric encryption technologies to ensure the integrity
and security of the blockchain data. Each block contains the root hash of the Merkle tree
and information like the preceding block’s hash, timestamp, nonce, block version number,
and current difficulty value. The Merkle tree [
39
] is a data structure constructed using hash
pointers to organize data. In a blockchain, transaction data within the block body are built
into a binary Merkle tree. The leaves are the hash values of the transaction data, while the
non-leaf nodes contain the sum of the hash values of its two child nodes, as is shown in
Figure 2. The purpose of organizing transactions with a Merkle tree is to quickly verify
whether any transaction has been tampered with. The use of the Merkle tree in blockchain
allows nodes to quickly summarize and verify the integrity and existence of transaction
data within a block [52].
B. Network layer
The network layer primarily furnishes mechanisms for information exchange among
each node within the blockchain network, including the P2P network mechanism, the
information communication mechanism, and the data verification mechanism. With a P2P
network, the messages are directly propagated between nodes. Each node has the same
functionality and status, and there is no centralized device. Each node is responsible for
routing, block data validation, block data propagation, transaction information packaging,
and discovering new nodes [
39
]. Under the P2P networking method, the system can still
operate normally even if any node breaks down.
C. Consensus layer
In a distributed blockchain system, the mechanism by which mutually distrustful
nodes reach a consensus on certain data or proposals within a specified time is called
the consensus mechanism. Blockchains have proposed the evaluation standard of the
“impossible triangle” for the consensus mechanism [
53
], that is, the three characteristics
of decentralization, scalability, and security cannot be satisfied simultaneously. Various
types of blockchains have different degrees of decentralization and numbers of nodes
participating in the consensus, so the consensus mechanisms they use are also distinct.
Public chains have a huge number of nodes participating in the consensus and a higher
degree of decentralization. They generally use consensus mechanisms such as proof of
work (PoW) [
54
], proof of stake (PoS) [
55
], and delegated proof of work (DPoS) [
56
]. Private
chains have fewer nodes and a lower degree of decentralization. They generally use
consensus mechanisms such as Paxos [
57
] and Raft [
58
]. Compared with public chains,
consortium chains have fewer nodes and the feature of “partial decentralization”. They
generally use the practical Byzantine fault tolerance (PBFT) [59] mechanism.
D. Incentive layer
Nodes within a blockchain network do not inherently contribute their computational
power to create new blocks unless there are incentives in place. These incentives are
governed by an incentive layer where miners are rewarded, which follows predefined
protocols. Generally, these rewards are granted upon the successful creation of a new block,
or they can be earned by charging fees for processing transactions. By providing these
economic incentives, miners are motivated to engage in mining activities with integrity.
E. Contract layer
The contract layer encompasses various forms of code, scripts, and smart contracts
responsible for governing the operations of the blockchain. Smart contracts are encoded
into the blockchain using computer languages and are equipped with trigger conditions
for specific events. When these events occur, smart contracts are executed automatically in
accordance with predefined rules. Smart contracts have the capability to autonomously

Future Internet 2023,15, 400 8 of 22
address matters within the blockchain network, eliminating the need for third-party inter-
vention and enhancing the blockchain’s autonomy and transparency.
F. Application layer
At present, blockchain technology is gradually entering the Blockchain 3.0 phase,
and various applications based on blockchain technology are developing steadily. The
digital currency application, which was the original use of blockchains, still attracts much
attention, and many people remain enthusiastic about investing in digital currencies.
Blockchains have been applied extensively in areas including finance [
60
,
61
], supply chain
management [62,63], IoT [64,65], etc.
It is important to emphasize that not all the layers mentioned above need to be
integrated into every blockchain. The lower three layers can be considered as foundational
layers that are crucial, while the upper three layers may not be necessary for all blockchain
implementations.
3. Blockchain-Based Federated Learning
In the traditional FL architecture, a central server is responsible for collecting, aggregat-
ing, and broadcasting the new global model, which may lead to the following problems: (a)
The stability of the central server might be affected by cloud service providers; (b) the cen-
tral server might show favoritism towards certain clients; and (c) a malicious central server
might poison the model or collect private information from clients. To address these issues,
the most direct solution is to remove the central server and let the client nodes handle the
corresponding tasks [
66
–
68
]. This requirement aligns well with the inherent characteristics
of blockchain technology. Recent studies have used the blockchain’s distributed storage
architecture as the foundational framework for FL [
69
–
79
]. By designing protocols on the
upper layer of the blockchain, they implement the task of model aggregation running for
clients. At the same time, the rational incentive mechanism in the blockchain provides a
technical solution to enhance the enthusiasm of all participants to actively participate in FL
model training.
3.1. Frameworks of BFL
This section summarizes and compares the blockchain-based federated learning (BFL)
frameworks proposed in the literature collected for this article, and analyzes their dif-
ferent design approaches. Figure 4shows the schematic diagram of the BFL framework
classification summarized in this article.
First, a traditional FL framework usually consists of a central server and multiple
users (or devices/clients). Early typical BFL frameworks generally used decentralized
blockchains to replace the central server in traditional FL frameworks. The main pur-
pose was to address the problems of single-point trust and failures caused by the central
server [
80
–
83
]. An example of this type of framework is shown in Figure 5. Users submit
their local models to the miners maintaining the blockchain. The miners carry out cross-
validation, model aggregation, and other steps, and produce a consistent global model
based on the consensus mechanism. They then use blocks to store and propagate this
global model. Users can download the consistent global model from the block to their
local devices for the next round of training. In addition to using blockchains to replace
the central server, this kind of typical framework usually has two features. Before model
aggregation, by introducing mechanisms such as cross-validation, it ensures that the local
models participating in the global model update conform to the direction of the global
model update, preventing users from using malicious models to jeopardize the security
of the global model. Furthermore, by introducing a reward mechanism, users can be
incentivized to contribute high-quality data and actively participate in training, effectively
alleviating the fairness problem of FL. This prevents users with different contributions from
receiving similar rewards, in case of users slacking off.

Future Internet 2023,15, 400 9 of 22
Figure 4. Types of BFL frameworks.
Figure 5. A generalized BFL paradigm.
On the basis of this typical framework, some BFL frameworks have led to further
innovation in areas such as consensus mechanisms and reward mechanisms. The BFL
framework with a committee consensus framework proposed in [
30
] does not adopt the
commonly used PoW consensus mechanism, but instead proposes the committee consensus
mechanism (CCM). A feature of this mechanism is the use of a committee composed
of some honest nodes to carry out local gradient verification of the model and block

Future Internet 2023,15, 400 10 of 22
generation. Because only a subset of nodes participate in local model verification and global
model updates, the overall efficiency of FL is significantly improved. This mechanism
requires nodes outside the committee to send their local models to committee nodes for
verification and scoring, and only allows qualified models to participate in global model
updates. By this mechanism, to enhance security, members of the committee are periodically
replaced based on node historical performance scores and smart contracts. Hieu et al. [
84
]
introduced deep reinforcement learning to find the optimal system parameters that can
minimize system delay, energy consumption, and total rewards, including recommended
data volume and energy consumption when users train local models, as well as block
generation rate. The FL, via the MEC-enabled blockchain network (FLChain) framework
proposed in [
85
], includes both mobile devices and edge devices. Mobile devices primarily
update local models using data samples on the devices, while edge devices provide more
abundant network resources for resource-limited mobile devices and also act as nodes in
the FLChain network to maintain the blockchain. FLChain utilizes the channel technology
in the consortium blockchain Hyperledger Fabric, leveraging the isolation feature of the
channels to enhance the security of global model training and provide a certain degree of
data privacy preserving. Li et al. [
86
] proposed a crowdsourcing BFL framework called the
crowd computing secure framework based on blockchain technology and FL (CrowdSFL).
Its main purpose is to reduce user costs during crowdsourcing and to ensure its security.
In CrowdSFL, the entire crowdsourcing system is built on the blockchain, and every
participant has an independent blockchain account. CrowdSFL introduces a data interaction
mode controlled by smart contracts, ensuring that the data are uploaded in the correct
format and stored in blocks.
The aforementioned BFL frameworks all use a single type of blockchain to replace
the central server used in traditional FL. In recent years, a few studies have proposed
BFL frameworks that replace central servers with more complex multi-level blockchains.
Lu et al. [87]
proposed a BFL framework based on a hybrid blockchain called the permis-
sioned blockchain and the local Directed Acyclic Graph (PermiDAG). In this framework,
the hybrid blockchain uses a permissioned blockchain running on the road side unit (RSU)
as the main chain, while allowing vehicle nodes to form multiple local directed acyclic
graphs (DAG). The permissioned blockchain serving as the main chain is responsible for
recording information related to data sharing and parameters related to global model ag-
gregation. Multiple local DAGs formed by vehicle nodes are used to enhance the efficiency
of data sharing, as well as to store data-sharing events and trained model parameters as
transactions in blocks. At the same time, based on the local DAG, neighboring vehicle
nodes communicate with each other, obtaining local models of nearby vehicles, and use
these models to enhance their own local models, realizing the asynchronous learning pro-
cess. Additionally, the blockchain-empowered federated edge learning (BFEL) framework
proposed in [
88
] also adopts a multi-level blockchain structure. This framework consists of
an application layer and a blockchain layer, with the application layer mainly responsible
for executing the FL process. The blockchain layer in this framework includes a main chain
based on a public blockchain and multiple sub-chains based on consortium blockchains.
By leveraging multiple sub-chains to set access permissions, this framework can enhance
the data-privacy-preserving capabilities and achieve performance isolation.
However, in some BFL frameworks, the blockchain neither directly replaces the central
server nor directly participates in the traditional FL process. For example, in the BFL frame-
work proposed in [
89
], the blockchain is only used to implement data sharing functions.
The blockchain in this framework contains three different types of transactions: (1) retrieval
transactions, allowing nodes to notify other nodes of the requested model information;
(2) model transactions, allowing nodes to pass model training data to other nodes; and
(3) data sharing transactions, allowing nodes to return the shared data to the requester.
Specifically, a data requester sends a data sharing request to the blockchain. The blockchain
conducts a retrieval transaction to check whether the cache already contains the correspond-
ing data: if it exists, the blockchain returns the query result and the requested data model

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directly to the requester and generates a data sharing transaction; if not, the blockchain per-
forms a multi-party information retrieval process, forms a model training committee, uses
model transactions for model training, generates the model required by the requester, and
caches the model for future needs while returning it to the requester. In the fine-grained FL
framework proposed in [
90
], the execution of FL mainly takes place in cloud nodes and fog
nodes. The blockchain in this framework does not directly participate in FL and is mainly
responsible for calculating and storing the reputations of various nodes participating in
FL. Moreover, in the BFL framework for equipment fault detection in the industrial IoT
proposed in [
91
], the blockchain is mainly used for trustworthy storage and verification
of client data. In this framework, clients regularly create Merkle trees to organize data
collected from sensors and store the Merkle root in the blockchain. In the event of future
disputes, the Merkle root stored in the blockchain can be used as evidence to help resolve
the disputes. The BFL framework based on a consortium blockchain proposed in [
92
] aims
to enhance edge computing capabilities in the digital twin wireless network model. This
framework consists of various types of terminal users, such as IoT devices, mobile devices,
base stations, and macro base stations. Base stations are responsible for executing the local
training of FL, while macro base stations act as the central servers for FL. Since FL cannot
solve the trust issue between twin terminal users, this framework introduces a consortium
blockchain to enhance system security, uses the blockchain to record data via the digital
twin process, and manages users by controlling access permissions.
3.2. Functions of BFL
In this section, we investigate the specific functions of BCFL with the perspective of
how blockchains mitigate the challenges faced by FL, which was introduced in Section 2.2
Specifically, we demonstrate this from five angles, including decentralization, incentive
mechanisms, attack resistance, privacy protection, and efficiency enhancement.
3.2.1. Decentralization
In FL, due to the central aggregation function of the server, once its device is subjected
to a single-point attack by adversaries, it poses a significant security risk to the entire
learning framework [
93
–
96
]. To enhance the security, trustworthiness, and reliability of
the framework, Majeed et al. [
85
] proposed a BFL architecture to improve the security
of FL. Basically, for each global model, the framework creates a new channel to store a
specific channel ledger, and concurrently creates a “global model state tree” to track weight
updates of the global model. Sharma et al. [
97
] utilized offline and online blockchains
to store temporary training data from a large number of nodes in real-time, a technique
based on a distributed multi-layer computing framework. The multi-layer and multi-
chain structure effectively reduces the impact of network failures and malicious attacks
on FL. Arachchige et al. [
98
] developed a framework called PriModChain by integrating
differential privacy, FL, the Ethereum blockchain, and smart contracts. It offers privacy,
security, and reliability for FL applications in the industrial IoT. However, the operating
efficiency of the framework restricts its further development. Lu et al. [
87
] introduced a
novel hybrid blockchain architecture composed of a permissioned blockchain and a local
DAG. It aims to enable effective data sharing in vehicular networks, thereby enhancing the
reliability of the learning model. Pokhrel et al. [
81
] introduced a multi-level trust framework
using a private blockchain to ensure end-to-end trustworthiness, from observation to
learning and verification of local model updates.
3.2.2. Incentive Mechanism
In order to solve the problem of a lack of incentive mechanism, BFL usually uses
blockchain technology to construct incentive mechanisms to achieve the expected behav-
iors, or penalty mechanisms towards abnormal behaviors, to stimulate the enthusiasm of
local users to contribute to the global model update [
99
,
100
]. Kim et al. [
101
] proposed
the BlockFL framework, in which each device uploads its local model updates to related

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miners in the blockchain network. Miners are responsible for exchanging and verifying
model updates, recording them in the blockchain, and providing corresponding rewards.
Kang et al. [102]
introduced the concept of reputation as a measure of client trustwor-
thiness, and used a multi-weighted subjective logic model to design a reputation-based
trustworthy client selection scheme. At the same time, they used the immutability of
blockchains to implement distributed reputation management and used contract theory to
provide corresponding rewards by analyzing the computational power investment and
model quality of the clients participating in model building. Weng et al. [
103
] proposed the
DeepChain scheme, distinguishing clients’ performances in terms of activity and compati-
bility during the training process and urging clients to send correct and high-quality model
updates. They also used blockchain technology to ensure model security and the audibility
of the training process, achieving the objectives of confidentiality, audibility, and fairness.
Kim et al. [104]
used blockchain technology to record all model updates comprehensively,
and provided generous rewards to incentivize users to participate in FL. They proposed
a weight-based client subset selection scheme, selecting clients for training based on the
accuracy of each client’s local model and the frequency of their participation in training,
achieving high stability and faster convergence speed. Zhan et al. [
105
] designed an incen-
tive mechanism based on deep reinforcement learning (DRL), applying traditional resource
allocation strategies to the specialized distributed scenario of FL, in order to achieve optimal
training strategies and pricing strategies for edge nodes.
3.2.3. Attack Resistance
To address the problem of poisoning attacks, BFL typically employs consensus mech-
anisms deployed in the blockchain to verify model updates, thus effectively preventing
poisoning attacks [
106
–
108
]. Qu et al. [
82
] proposed replacing the central server with a
blockchain system to utilize the blockchain’s immutable nature to eliminate poisoning
attacks. Kang et al. [
88
] introduced a proof-of-validation (PoV) consensus mechanism used
to collaboratively verify the update quality of local models among predefined miners. In
this scheme, only validated model updates can be stored in a block, thereby preventing
poisoning attacks. To reduce malicious poisoning model updates, Zhao et al. [
109
] pro-
posed a reputation-based crowdsourcing incentive mechanism. Under this mechanism,
if a user is detected to be making malicious updates, their update model will be rejected.
They will not only miss out on rewards for that update round, but will also have their
reputation reduced, affecting future profits and leading to penalties. Zhang et al. [
110
]
introduced a scoring mechanism to determine whether a device is malicious and might
launch poisoning attacks, thereby selecting trainers to participate in the model update
to resist such attacks.
Shayan et al. [111]
proposed a multi-Krum consensus mechanism,
which rejects model updates that go against the direction of most model updates. In each
update round, a validation peer committee is elected by majority vote. This committee
uses multi-Krum to reject malicious model updates, thus preventing poisoning attacks.
Chen et al. [
112
] introduced a decentralized validation mechanism to verify local model
updates. This mechanism votes on the validity of each model, then uses the voting results
to eliminate potential malicious devices.
3.2.4. Privacy Protection
To forestall privacy breaches, certain BFL schemes incorporate additional privacy
protocols [
79
,
113
–
115
]. For instance, Martinez et al. [
116
] implemented homomorphic
encryption to safeguard the privacy of the training model. Shayan et al. [
111
] developed
Biscotti, a decentralized P2P scheme based on blockchain, employing a verifiable secret
sharing scheme for secure model aggregation to fortify individual privacy. Blockchain
and FL technologies were combined by Ren et al. [
117
] to devise an intrusion detection
algorithm suitable for lightweight network devices, with the aim of safeguarding the
data privacy of network users during data sharing. Feng et al. [
78
] harnessed the decen-
tralization and tamper-proof attributes of blockchains, storing data records and critical

Future Internet 2023,15, 400 13 of 22
information on the blockchain while the complete data were encrypted and stored in a
distributed database, ensuring secure storage to prevent the leakage of users’ private data.
Weng et al. [
103
] employed the Paillier algorithm to encrypt users’ model parameters,
subsequently uploading them to the blockchain. After the completion of the model updates,
collaborative decryption was executed by a consortium of users. In the context of data
security and sharing requisites in the IIoT and Smart Transportation, Lu et al. [
89
] and
Qi et al. [118]
deployed local differential privacy techniques. They introduced noise to the
raw data prior to feature extraction and sharing to thwart privacy attacks. The Adaptive
Differential Privacy FedAvg (ADPFe-dAvg) algorithm was presented by Zhang et al. [
119
]
to protect the client’s historical data during the entire training phase and prevent member
inference attacks in visual object identification modeling. ADPFedAvg introduced user-
level differential privacy technology, complemented by adaptive clipping technology. To
establish a data-privacy-preserving mechanism, Mahmood et al. [
120
] encrypted all data
via a public key infrastructure (PKI) comprising a public key and a private key, achieving a
BFL mechanism that preserved data privacy with multi-layered security.
3.2.5. Efficiency Enhancement
Lastly, to address the issue of inefficiency, BFL schemes often employ various methods
to reduce the amount of data that need to be transmitted. The approach proposed in [
82
]
stores specific related data in an off-chain distributed hash table, and only pointers are
stored on the blockchain, thereby reducing the data transmission volume. Lu et al. [
87
] intro-
duced an asynchronous FL scheme for the edge data learning model which further enhances
the efficiency of FL by selecting participating nodes. Li et al. [
30
] introduced the committee
consensus mechanism, which verifies local gradients before attaching them to the chain.
Under this mechanism, only a few nodes are used to verify model updates, eliminating the
need to broadcast to every node and reach a consensus and, thus, improving the efficiency
of model verification. Kang et al. [
88
] described a gradient compression scheme, which can
enhance the communication efficiency of blockchain-authorized federated edge learning
without compromising learning accuracy. Furthermore, Kumar et al. [
121
] proposed a
method that incorporates hyperparameter optimization and elastic weight consolidation
into federated learning to enhance the accuracy and efficiency of the model training.
The integration of FL and blockchains makes the system a comprehensive closed-
loop learning mechanism. On the one hand, FL technology provides a secure, cross-
domain sharing solution for participants with private data. On the other hand, blockchain
technology, serving as the core database, provides participants with application needs such
as secure storage, trust management, fine-grained differentiation, and incentive returns,
encouraging users with data to actively participate in data federation.
4. Applications
Currently, BFL technology has been applied in many industry areas. This article
summarizes the application prospects of the current BFL technology in areas such as
the Internet of Things (IoT), Industrial Internet of Things (IIoT), healthcare services, and
Internet of Vehicles (IoV).
4.1. Internet of Things
In the realm of IoT, devices are decentralized, and consequently, conducting model
training on these devices necessitates both timely and secure data access, as well as robust
model generalization capabilities. The research pertaining to the application of BFL within
the IoT domain predominantly centers on addressing concerns related to data security,
resource allocation, communication protocols, and failure detection [
27
,
71
,
108
,
122
]. The
overarching objective of these efforts is to empower IoT devices to collaboratively train
models that exhibit high performance. Lu et al. [
89
] constructed a distributed multi-party
data sharing model that further ensures the authenticity of data through differential privacy,
allowing devices to retrieve data securely and accurately. Instead of the common PoW

Future Internet 2023,15, 400 14 of 22
consensus algorithm, the proof of training quality (PoQ) consensus algorithm in [
89
] is
used to verify training models, aiming to improve the utilization efficiency of computa-
tional resources. To help household appliance manufacturers improve service quality and
optimize appliance functions, Zhao et al. [
109
] introduced a hierarchical crowdsourcing
FL system, utilizing blockchain technology to prevent malicious model updates. To make
the 6G network more secure and efficiently apply it to the IoT, Dai et al. [
83
] proposed a
combination of a blockchain and FL, integrating mobile edge computing and device to
device (D2D) communication, to address the challenges faced by the 6G network.
4.2. Industrial Internet of Things
The IIoT encompasses an intricate network of interconnected sensors, equipment,
actuators, and other intelligent components. These components facilitate adaptive decision-
making and continuous status tracking [
123
,
124
], playing a pivotal role in the digital
transformation and intelligentization of the contemporary manufacturing industry. In
a study conducted by Lu et al. [
89
], BlockFed was employed to facilitate data sharing
within the domain of IIoT. The data-sharing challenge was approached by framing it as an
ML problem, incorporating privacy-preserving FL, and integrating FL into the consensus
mechanism of a permissioned blockchain. The computational effort required for the
consensus was also utilized for federated training. In the context of fault detection scenarios
in IIoT, Zhang et al. [
91
] proposed a federated averaging algorithm called Centroid Distance
Weighted Federated Averaging. This algorithm takes into account the distance between
negative and positive classes within each client dataset, thereby mitigating the impact of
data heterogeneity challenges in IIoT device fault detection. Additionally, Lu et al. [
92
]
recognized the challenges posed by unreliable communication channels, computational
resource constraints, and the intricacies associated with establishing trust among users
within the context of IIoT. To address these issues, they developed an FL framework
for collaborative computation empowered by blockchain technology. This framework
substantially elevated the system’s reliability, security, and privacy.
4.3. Smart Healthcare
BFL can also bring significant advancements to healthcare services. Typically, remote
patient monitoring or certain AI-assisted diagnoses require a large amount of patient
disease information. However, many medical records contain sensitive information about
the patient, and these data have high intrinsic value for certain attackers. As a result, BFL
is gradually being applied to the medical field [
75
,
107
,
125
]. Passerat et al. [
126
] proposed
a BFL scheme for healthcare alliances, establishing a set of enterprise-level blockchain
components compatible with the Ethereum ecosystem and integrating a series of privacy
protection techniques. It also introduced a new secure aggregation protocol designed to run
within AMD’s trusted hardware environment, secure encrypted virtualization (SEV), to
ensure the security of private data. El Rifai et al. [
127
] introduced a BFL framework in the
medical field, applying smart contracts to the data aggregation process of FL algorithms.
This ensures transparency and permission during data sharing, predicting diabetes risk
based on training with substantial patient information. Furthermore, Polap et al. [
128
]
developed a lightweight security and privacy algorithm for Internet of Medical Things
(IoMT) devices based on BFL. Rahman et al. [
129
] not only presented a trustworthy BFL
framework applicable to the IoMT, but also designed a COVID-19 application for data
classification by which we can learn about global models related to COVID-19 diagnoses.
This scheme includes a trustworthy and tamper-proof gradient mining method and a
decentralized consensus-based aggregator, and adds extra security for blockchain nodes
responsible for aggregation. Aich et al. [
130
] also introduced a BFL scheme for healthcare,
aiming to protect and share patients’ medical information by building a real-time global
application model. In addition, Kumar et al. [
131
] proposed a BFL framework that uses the
latest data to segment and classify lung CT scans based on capsule networks, sharing data

Future Internet 2023,15, 400 15 of 22
between hospitals to improve COVID-19 detection rates while ensuring patient privacy
protection.
4.4. Internet of Vehicles
BFL solutions have been widely applied to the IoV to facilitate data sharing and
autonomous driving [
81
,
118
,
132
]. Pokhrel et al. [
81
] proposed a fully decentralized BFL
framework. This framework achieves end-to-end trustworthy communication within the
IoV, and the communication latency remains within an acceptable range, thus promoting
effective communication for automated vehicles. They use BFL to verify model updates
in on-vehicle machine learning (oVML), enhancing the performance and privacy security
of automated vehicles. Lu et al. [
87
] introduced a BFL framework composed of a primary
permissioned blockchain maintained by roadside units and a local DAG run by vehicles,
aiming for efficient data sharing in the IoV. Additionally, Lu et al. also proposed an
asynchronous FL scheme based on edge data. By using the Delegated Proof of Stake
(DPoS), it selects optimized participating nodes, thereby improving the efficiency of FL.
In [
133
], a blockchain-based hierarchical FL algorithm is introduced which reduces storage
consumption and improves training accuracy. The proposed knowledge-sharing method
based on BFL enhances the reliability and security of in-vehicle networks. Using the proof
of learning (PoL) consensus mechanism, a lightweight blockchain was realized, preventing
the wastage of computational power.
Additionally, BFL is gradually being expanded to various domains. In the field of
content caching, Cui et al. [
134
] presented a new algorithm called the blockchain-assisted
compressed algorithm of FL, applied for content caching (CREAT). This blockchain-assisted
FL algorithm aims to predict cache files and enhance the cache hit rate. In the domain
of location prediction, the scheme proposed in [
135
] utilized BFL for local training on
users’ mobile devices. This approach safeguards user privacy while making better use
of the data for more accurate location predictions. In the realm of mobile crowd sensing,
Wang et al. [
136
] introduced the secure FL for an unmanned aerial vehicle (UAV)-assisted
crowdsensing (SFAC) framework. This is a secure FL architecture for UAV-assisted mobile
crowd sensing (MCS), employing local differential privacy to protect the privacy of data
providers. Moreover, BFL has been applied to disaster response. The study in [
137
]
proposed a blockchain-authorized BFL framework that will implement a disaster response
system using wireless mobile modules on UAVs using future 6G networks. Additionally,
BFL has also been adopted in the news recommendation field. Wang et al. [
138
] presented
a cloud-edge collaborative filtering recommendation system based on FL. This system
incorporates noise into the training model using differential privacy technology, further
preventing data privacy exposure.
5. Challenges and Future Directions
The introduction of blockchains has helped address some of the significant issues in
traditional FL. However, the integration of blockchains with FL also confronts challenges
posed by blockchain technology itself, awaiting continuous exploration by researchers.
5.1. Privacy Concerns
The public blockchain ledger allows for secure and reliable data processing, but the
collected FL training data can be accessed publicly and are available for all participants to
use. This might lead to issues of circumventing privacy protection mechanisms. Further-
more, the ubiquitous sensing systems in the IoT continuously collect personal and sensitive
data from consumers. Placing these data into an open ledger may lead to privacy concerns.
Using a private blockchain ledger can ensure data privacy by enabling encryption and
allowing controlled access to the ledger. However, such private blockchain platforms will
limit the accuracy of processing and execution in the FL system, thereby affecting the access
to and disclosure of vast amounts of data needed for decision making and analysis.

Future Internet 2023,15, 400 16 of 22
Most blockchain systems lack sufficiently robust privacy protection mechanisms.
Therefore, BFL frameworks need to incorporate privacy protection technologies like differ-
ential privacy and homomorphic encryption to provide additional protection to the data
placed on the blockchain. For instance, in the literature [
89
,
111
], differential privacy is
employed during the model extraction process by adding noise to preserve the privacy of
individual data. Shayan et al. [
111
] also introduced a verifiable secret-sharing scheme for
secure model aggregation. In addition, Martinez et al. [
116
] used homomorphic encryption
algorithms to encrypt training data for privacy protection. For existing BFL frameworks,
striking a better balance between the cost of privacy protection and training accuracy
remains a crucial issue to address.
5.2. Efficiency, Performance, Scalability
When incorporating specific privacy encryption algorithms into FL systems, there is
a substantial deceleration in the system’s processing speed. This has made the practical
application of robust privacy protection mechanisms in FL systems particularly challenging.
In the realm of blockchain systems, cryptocurrency platforms such as Bitcoin’s blockchain
can execute an average of four transactions per second, while Ethereum manages roughly
twelve. When compared with VISA’s capability to process millions of transactions every
second, such a performance is obviously unsatisfactory. Current research focuses on side-
chains (also known as off-chains) [
139
] to enhance blockchain performance, facilitating
rapid settlements between parties outside the main chain, with daily consolidations on the
primary chain. Emerging blockchain variants have considerably refined their consensus
algorithms for mining nodes. Platforms like Algorand [
140
] and IOTA [
141
], for instance,
offer superior performance compared to the Ethereum and Hyperledger blockchains. Nev-
ertheless, there remains a pressing need to amplify scalability, address extant performance
issues, and thereby elevate the integrated system’s performance when paired with federated
learning systems.
Additionally, the encryption/decryption processes inherent to blockchains, coupled
with the PoW mechanism, substantially hamper the efficiency of model training due to their
complexity. For more sophisticated models, encryption and the subsequent transmission of
model parameters consume significant time. Furthermore, the storage of large-scale models
during iterative processes in blockchains incurs elevated storage costs. Future iterations
of BFL systems necessitate further enhancements to their practicality, striving to augment
their tangible value in real-world applications.
5.3. Security Concerns
A BFL system may encounter issues related to the abuse of decentralized authority.
While blockchain technology offers reliable solutions for protecting all parties involved
in the federated learning system and parameter exchange during predictive analysis, the
entire blockchain system is still vulnerable to network attacks like the 51% attack [
142
].
Furthermore, the consensus mechanism, depending on the mining power, may also be
compromised, leading to a concentration of the originally decentralized platform around
mining fields that control consensus and settlement. This security issue is more pronounced
in public blockchains like Ethereum and Bitcoin, whereas private blockchain platforms are
less affected because consensus protocols are predefined among the parties.
While BFL frameworks can offer some resistance against poisoning attacks through
well-designed consensus mechanisms, many blockchain consensus algorithms themselves
face security risks. For instance, in the most common Proof of Work (PoW) consensus
algorithm, miners may experience delays in receiving blocks, which can lead to forking
issues. In a recent study [
80
], the introduction of ACK (ACKnowledge character) was
proposed to determine, within a waiting period, whether a fork has occurred. If a fork is
detected, the mining process is restarted, mitigating the problem. Some recent research has
put forward new consensus algorithms; however, the security of these algorithms often

Future Internet 2023,15, 400 17 of 22
lacks theoretical proof and practical validation. Consequently, the challenge of developing
provably secure BFL consensus algorithms remains an urgent issue to address.
6. Conclusions
This article elucidates the current state of the research domain that integrates blockchain
technology with FL. Through an extensive survey of existing literature in the BFL realm, a
comprehensive analysis and comparison was conducted across the foundational architec-
ture, core technologies, and prospective applications. Currently, the BFL domain remains
in its nascent stages. The majority of research endeavors merely integrate blockchain
techniques to address the singular trust issue inherent to FL, lacking further exploration
concerning privacy, efficiency, and security. Furthermore, a significant portion of the stud-
ies remains theoretical, and some proposed BFL frameworks are not exhaustive, thereby
calling the practical applicability of the present BFL techniques into question. With the
rapid advancements in both blockchain and FL, two pivotal domains, BFL, as their inter-
disciplinary junction, has the potential to distill the technical prowess of each, and further
fosters innovative techniques that in turn nourish both fields. This paradigm establishes
a trustworthy privacy-preserving learning model, heralding transformative changes for
numerous application areas.
Author Contributions:
Ideation, Y.H. and J.H.; literature research, L.W. and W.R.; writing-original
draft, L.W.; writing-review and editing, W.R., J.H. and Y.H. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research was funded by National Natural Science Foundation of China, Joint Fund
Project, U22B2021.
Data Availability Statement:
No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Conflicts of Interest: The authors declare no conflict of interest.
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