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Cross-Device Synchronization Techniques for Distributed Machine
Learning with Privacy Constraints
Ruilin Nong Xingzu Liu1Mingbang Wang
Yanming Liu Jiyuan Li David Williams3∗
1University of Florida 2University of Pennsylvania
david.willams0795@gmail.com
Abstract
Distributed machine learning has become
increasingly vital as devices generate vast
amounts of data. However, ensuring privacy
during model synchronization represents a sig-
nificant hurdle. In this paper, we present a novel
framework for Cross-Device Synchronization
Techniques that focuses on maintaining privacy
while enhancing collaboration among devices.
Our method employs advanced cryptographic
techniques, where each device computes local
model updates, encrypts them, and sends them
to a central server. The server aggregates these
encrypted updates without needing to decrypt
them, safeguarding sensitive data throughout
the process. By implementing secure multi-
party computation (SMPC) alongside differen-
tial privacy mechanisms, we enable devices to
work together without exposing individual data
points. Through extensive experiments, we val-
idate our synchronization techniques, demon-
strating both robust model accuracy and pro-
tecting privacy. Furthermore, we investigate
the balance between communication efficiency
and privacy levels, offering valuable insights
for optimizing distributed machine learning sys-
tems. Our findings underscore the practicality
of implementing privacy-preserving synchro-
nization solutions in environments where data
confidentiality is essential.
1 Introduction
Distributed machine learning (DML) has seen sig-
nificant innovations aimed at enhancing synchro-
nization while addressing privacy concerns. Tech-
niques such as federated learning (FL) are designed
to ensure that data remains on local devices, reduc-
ing the risk of exposure. In particular, the elastic
virtualized FL (EV-FL) architecture shows promise
by optimizing resource provisioning for various
DML services while leveraging the capabilities of
Open RAN systems(Abdisarabshali et al.,2023).
Additionally, exploring serverless computing
models in conjunction with a peer-to-peer architec-
ture can improve fault tolerance and reduce overall
costs in DML training setups(Barrak et al.,2023).
Tools like the CDML design toolbox provide struc-
tured guidance for developing collaborative DML
systems to fulfill specific use case requirements,
allowing for better coordination across devices(Jin
et al.,2023).
The transition from distributed machine learn-
ing to more advanced distributed deep learning
methods highlights ongoing limitations in existing
algorithms and emphasizes the need for future re-
search to eliminate these constraints(Dehghani and
Yazdanparast,2023).
However, achieving effective machine learning
while ensuring privacy poses numerous challenges.
Techniques such as cryptography and differential
privacy are essential for protecting information
used in distributed optimization and learning, al-
though they come with trade-offs regarding pri-
vacy and optimization accuracy (Chen and Wang,
2024). Fully Homomorphic Encryption (FHE) has
also been explored in privacy-preserving federated
learning, providing a method for model aggregation
without compromising data security (Rahulamath-
avan et al.,2023). Evaluations of cryptographic
methods such as homomorphic encryption and mul-
tiparty computation in the medical sector under-
line the importance of selecting the right privacy-
preserving technique based on specific applications
(Zalonis et al.,2022). Furthermore, the need to de-
tect malicious users in federated learning is critical,
and the introduction of protocols like MUD-PQFed
highlights strategies for tackling model corruption
while maintaining privacy (Ma et al.,2022). De-
spite these advancements, balancing privacy con-
straints with effective synchronization and accurate
learning remains an ongoing challenge.
We introduce a novel framework for Cross-
Device Synchronization Techniques in Distributed
Machine Learning that prioritizes privacy con-
straints. Our approach utilizes advanced crypto-
graphic techniques to ensure that model updates
shared across devices remain confidential. Each
device computes local updates from its data and en-
crypts them before sending to a central server. The
server aggregates these encrypted updates without
decrypting them, ensuring that sensitive data is not
exposed during the synchronization process. We
implement secure multi-party computation (SMPC)
and differential privacy mechanisms to further en-
hance privacy, allowing devices to collaborate with-
out revealing individual data points. Extensive ex-
periments demonstrate the effectiveness of our syn-
chronization techniques in maintaining high model
accuracy while ensuring robust privacy. Addition-
ally, we analyze the trade-offs between commu-
nication efficiency and privacy levels, providing
insights into optimizing distributed machine learn-
ing systems. The results underline the feasibil-
ity of privacy-preserving synchronization in real-
world applications, where data confidentiality is
paramount.
Our Contributions. Our contributions can be
articulated as follows:
•
We propose a comprehensive framework for
Cross-Device Synchronization in Distributed Ma-
chine Learning that emphasizes robust privacy
constraints through the use of advanced crypto-
graphic methods.
•
Our approach ensures that model updates are se-
curely encrypted by each device, allowing for
aggregation at a central server without compro-
mising the confidentiality of sensitive data.
•
We demonstrate the implementation of secure
multi-party computation (SMPC) and differen-
tial privacy, fostering an environment for secure
collaboration among devices while maintaining
individual privacy.
•
Through extensive experimental evaluations, we
assess the effectiveness of our methods in pre-
serving model accuracy alongside privacy, offer-
ing insights into balancing communication effi-
ciency with privacy requirements in distributed
learning contexts.
2 Related Work
2.1 Distributed Learning Techniques
The application of distributed learning techniques
has generated significant advancements across var-
ious domains, particularly in enhancing perfor-
mance efficiency and accuracy. Techniques such
as the two Deep Learning algorithms, CNN and
FNN, showcased a remarkable accuracy of up to
99% for mitigating DDoS attacks in 5G networks
and IoT devices (Alzhrani and Alliheedi,2023).
Efforts in failure-tolerant distributed learning have
resulted in improved anomaly detection capabili-
ties, with new methods outperforming traditional
approaches by as much as 8% while also reduc-
ing communication costs (Katzef et al.,2023). In
the context of human activity recognition, com-
prehensive evaluations demonstrated that specific
machine learning and deep learning classifiers, like
Linear Support Vector Classifier and Gated Recur-
rent Unit, could achieve superior accuracy when
deployed in distributed settings (Uday et al.,2022).
Privacy concerns in such distributed frameworks
have been addressed through innovative protocols,
allowing clients to independently decide their par-
ticipation without requiring a trusted aggregator,
thus enhancing robustness against client dropouts
(Liew et al.,2022).
2.2 Privacy-Preserving ML
A recently developed system, P4L, facilitates a
user-driven, decentralized learning approach, en-
suring privacy without the need for traditional in-
frastructures or differential privacy mechanisms
(Arapakis et al.,2023). In the context of secure
instance encoding, a new measure based on Fisher
information has been proposed to enhance privacy
guarantees(Jeon et al.,2020), making it practical
and intuitive for bounding invertibility both theo-
retically and empirically (Maeng et al.,2023). Fur-
thermore, a highly effective pruning method known
as Artemis has been introduced, optimizing deep
neural network models for homomorphic encryp-
tion (HE) applications and achieving substantial ef-
ficiency improvements (Jeon et al.,2023;Luo et al.,
2023). In the healthcare sector, advancements have
been made for privacy-preserving cancer predic-
tion by leveraging domain knowledge and efficient
algorithms to manage high-dimensional genomic
data (Sarkar et al.,2022). The importance of fair-
ness in machine learning audits has been addressed
through the development of PrivFair, a library de-
signed to maintain confidentiality during the fair-
ness auditing process (Pentyala et al.,2022a). Ad-
ditionally, strategies to maintain group fairness in
federated learning scenarios have been introduced,
allowing the training of models with complete pri-
vacy guarantees without exposing sensitive infor-
mation (Pentyala et al.,2022b). Synthetic datasets
have been validated as a viable option for training
machine learning models(Ni et al.,2024), effec-
tively preserving the privacy of the original data
while ensuring satisfactory performance (Soufleri
et al.,2022).
2.3 Cross-Device Communication
Advancements in communication optimization
techniques for federated learning are pivotal for en-
hancing performance in scenarios where multiple
devices collaborate. Strategies like DoCoFL signif-
icantly compress downlink communication while
maintaining competitive accuracy compared to un-
compressed baselines, demonstrating substantial
bi-directional bandwidth savings (Dorfman et al.,
2023). In a similar vein, conducting more than
one communication round per cohort in federated
learning, as explored by Cohort Squeeze, can dras-
tically reduce total communication costs, achiev-
ing up to a 74% decrease (Yi et al.,2024). The
proposed SPAM algorithm also contributes to ef-
ficiency by addressing non-convex loss functions
without requiring smoothness, and it offers advan-
tages when clients have similar datasets (Karag-
ulyan et al.,2024). Moreover, innovations like the
Buffered Asynchronous Secure Aggregation proto-
col ensure secure communication while enabling
clients to interact with the server in just one round,
thus streamlining the process (Wang et al.,2024).
3 Methodology
The increasing importance of privacy in distributed
machine learning necessitates advanced synchro-
nization techniques that protect sensitive informa-
tion. Our framework for Cross-Device Synchro-
nization Techniques emphasizes the use of crypto-
graphic methods to secure model updates shared
amongst devices. Each device is responsible for cal-
culating local updates based on its unique dataset,
encrypting these updates prior to transmission. The
central server plays a crucial role in aggregating
these encrypted updates without accessing the raw
data. Through the implementation of secure multi-
party computation (SMPC) and differential privacy,
we elevate the confidentiality of individual data
points, enabling devices to work together securely.
Experiments conducted validate the high accuracy
of model performance while adhering to strict pri-
vacy standards. Furthermore, we explore the bal-
ance between communication efficiency and vary-
ing levels of privacy, yielding valuable insights for
the optimization of distributed machine learning
frameworks. Our findings affirm the practicality of
privacy-centric synchronization methods in scenar-
ios where safeguarding data is essential.
3.1 Privacy-Preserving Techniques
To achieve robust privacy-preserving synchroniza-
tion in distributed machine learning, we employ
advanced cryptographic methods to secure model
updates exchanged among devices. Let
Ui
denote
the local update computed by device
i
, derived
from its local dataset
Di
. The update is encrypted
using an encryption function
E
, such that the en-
crypted update Eican be expressed as:
Ei=E(Ui).(1)
When each device sends its encrypted update
to a central server, the server aggregates these en-
crypted values without decrypting them. The ag-
gregation function
A
computes the aggregated en-
crypted update:
A=A(E1, E2, ..., En).(2)
This ensures that the confidential nature of indi-
vidual updates is preserved throughout the process.
To enhance this privacy further, we incorporate se-
cure multi-party computation (SMPC), enabling
the aggregation process to recruit multiple parties
in the computation of the encrypted updates with-
out exposing individual contributions. This allows
for computing the aggregated model update
U
as
follows:
U=D(A(E1, E2, ..., En)),(3)
where
D
is the corresponding decryption func-
tion applied after aggregation in a secure environ-
ment. Additionally, differential privacy mecha-
nisms are incorporated to add noise
ϵ
to the local
updates before encryption, safeguarding individual
data points where:
U′
i=Ui+ϵi,(4)
where
ϵi
controls the level of noise introduced
to fair privacy guarantees. Through this systematic
approach, we maintain the confidentiality of indi-
vidual data points while ensuring accurate model
synchronizations across devices.
3.2 Secure Multi-Party Computation
In our framework for Cross-Device Synchro-
nization Techniques, we leverage Secure Multi-
Party Computation (SMPC) to enable collaborative
model updates while preserving privacy. Each par-
ticipating device, indexed by
i
, computes a local
model update
ui
based on its local data
Di
and
subsequently encrypts this update using a secure
encryption scheme E:
ˆui=E(ui;k)(5)
where
k
is the encryption key. These encrypted
updates
ˆui
are then transmitted to a central server.
The server’s role is to aggregate these updates while
maintaining the confidentiality of the individual
contributions. The aggregation can be represented
as a function A:
ˆuagg =A({ˆu1,ˆu2,...,ˆun})(6)
Importantly, the aggregation function
A
operates
directly on the encrypted data without requiring
decryption, ensuring that the individual updates
remain secure. For instance, through additive ho-
momorphic encryption, we can compute:
ˆuagg =
n
X
i=1
ˆui(7)
This aggregated result is then securely transmit-
ted back to devices for updating their models. Fur-
thermore, our approach incorporates differential
privacy by adding noise
N
to the aggregated up-
dates before sharing them:
˜uagg = ˆuagg +N(8)
This mechanism ensures that even if an adver-
sary intercepts the aggregated update, it remains
difficult to infer individual data points, thus bol-
stering the privacy guarantees of our synchroniza-
tion technique. By employing SMPC alongside
differential privacy, we provide a robust solution
for privacy-preserving collaborative learning across
distributed devices.
3.3 Communication Efficiency
To evaluate the communication efficiency in our
proposed Cross-Device Synchronization Tech-
niques, we define the overall communication cost
C
associated with transmitting encrypted updates
from
N
devices to the central server. Let
Ui
rep-
resent the local update computed by device
i
, and
E(Ui)
be the encrypted update sent to the server.
The total communication cost can be formalized
as:
C=
N
X
i=1
size(E(Ui)),(9)
where
size(E(Ui))
denotes the size of the en-
crypted update from device
i
. By utilizing ad-
vanced cryptographic techniques, we can minimize
the size of the encrypted updates through efficient
encryption algorithms which can be represented as
a function
compress(E(Ui))
. Consequently, the
communication cost can be further optimized by
reformulating as:
Coptimized =
N
X
i=1
compress(E(Ui)).(10)
This compression allows for efficient use of
bandwidth and reduces latency during the synchro-
nization process. Additionally, we focus on bal-
ancing the trade-off between compression levels
and the resultant model accuracy. Given the con-
straints of differential privacy, the trade-off can be
characterized by a function
T(p)
that determines
the relation between privacy level
p
and communi-
cation cost:
T(p) = Coptimized
p,(11)
where higher privacy levels typically lead to
larger encrypted data sizes. Addressing these equa-
tions enables the efficient management of resources
in distributed machine learning systems, showcas-
ing the need for strategic optimization of commu-
nication efforts while maintaining rigorous privacy
standards.
4 Experimental Setup
4.1 Datasets
To evaluate performance and assess quality in the
context of cross-device synchronization techniques
for distributed machine learning with privacy con-
straints, we utilize several key datasets. These
include the Deep Hashing Network for Unsuper-
vised Domain Adaptation (Venkateswara et al.,
2017), which focuses on learning representative
hash codes for domain adaptation, and Adaptiope,
which addresses dataset annotation challenges in
unsupervised domain adaptation (Ringwald and
Method Dataset Communication Latency (ms) Model Accuracy (%) Number of Devices Learning Rate Differential Privacy Noise Multiplier
DASH Deep Hashing Network 200 83.2 50 1×10−30.1
LOOP-MAC Adaptiope 180 85.0 50 1×10−30.1
Distributed Learning Review RECALL 220 81.5 50 1×10−30.1
Smart Skin Control ImDrug 210 82.8 50 1×10−30.1
Certification Scheme SensitiveNets 250 79.6 50 1×10−30.1
Proposed Method Combined Datasets 175 87.5 50 1×10−30.1
Table 1: Comparison of Cross-Device Synchronization Techniques for Distributed Machine Learning with Privacy
Constraints
Stiefelhagen,2021). Additionally, RECALL pro-
poses a rehearsal-free method for continual learn-
ing without retaining previous sequences (Knauer
et al.,2022). The impacts of catastrophic forget-
ting are studied in the context of gradient-based
neural networks and dropout algorithms (Goodfel-
low et al.,2013). The ImDrug benchmark facili-
tates deep learning in the context of imbalanced
data in drug discovery (Li et al.,2022), while Sen-
sitiveNets presents a privacy-preserving approach
that maintains data utility while suppressing sensi-
tive information (Morales et al.,2019).
4.2 Baselines
To perform an evaluation of cross-device synchro-
nization techniques for distributed machine learn-
ing under privacy constraints, we compare our pro-
posed method with the following baselines:
DASH (Sander et al.,2023) introduces a fast
and distributed private machine learning inference
scheme utilizing arithmetic garbled circuits, focus-
ing on enhancing the efficiency of protected dis-
tributed ML systems, particularly with deep convo-
lutional neural networks.
LOOP-MAC (Li and Mohammadi,2023) presents
a multi-agent coordination framework for virtual
power plants, where each agent optimizes the op-
eration of distributed energy resources using neu-
ral network approximators, aiming to improve the
speed of solution searches.
Distributed Learning Review (Subasi et al.,2023)
provides an overview of the latest advancements
in machine learning algorithms, encompassing dis-
tributed and federated learning paradigms, along-
side their various applications and frameworks.
Smart Skin Control (Li,2023) employs a novel
variant of particle swarm optimization for control-
ling a Smart Skin flow control device, which is
characterized by distributed-input and distributed-
output systems, integrated with machine learning
techniques.
Certification Scheme (Anisetti et al.,2023) re-
views current certification challenges in machine
learning-based distributed systems and proposes a
certification approach that addresses these deficien-
cies and opens discussions on unresolved research
issues.
4.3 Models
Our research investigates distributed machine learn-
ing frameworks emphasizing cross-device synchro-
nization techniques while maintaining stringent pri-
vacy constraints. We utilize state-of-the-art models
like GPT-3.5 (gpt-3.5-turbo-0125) and Llama-3 to
conduct simulations and benchmarks. For privacy-
preserving data management, we implement dif-
ferential privacy techniques to ensure that model
updates from individual devices do not expose sen-
sitive information. Our evaluation includes a focus
on synchronization latency and model accuracy
across various network conditions, optimizing the
trade-offs between performance and privacy safe-
guards with a specific emphasis on decentralized
data sources.
4.4 Implements
In our experiments, each device utilized a local
batch size of 64 for computing model updates, fol-
lowed by encryption before communication. The
encryption algorithm employed is based on homo-
morphic encryption techniques, specifically Paillier
cryptosystem, ensuring that we carry out computa-
tions on encrypted data. We set the communication
round to 10 for synchronizing updates across de-
vices. The learning rate for model updates is fixed
at
1×10−3
, with gradient clipping applied at a
threshold of 5 to manage explosive gradients. We
configured the differential privacy mechanism with
a noise multiplier of 0.1, ensuring that the updates
maintain a balance between model utility and pri-
vacy. The total number of participating devices
in our study was set at 50, creating a diverse and
distributed training environment. For evaluation,
we run the synchronization protocol over a fixed
duration of 100 epochs, measuring latency in mil-
liseconds. The model accuracy is assessed every 10
Component Dataset Communication Latency (ms) Model Accuracy (%) Number of Devices Learning Rate Differential Privacy Noise Multiplier
Without Encryption Combined Datasets 250 76.2 50 1×10−30.1
Without SMPC Combined Datasets 210 82.0 50 1×10−30.1
Without Differential Privacy Combined Datasets 230 78.9 50 1×10−30.1
Without Aggregation Combined Datasets 240 75.4 50 1×10−30.1
Without Multi-Device Support Combined Datasets 200 80.0 10 1×10−30.1
Proposed Method Combined Datasets 175 87.5 50 1×10−30.1
Table 2: Ablation study of different components in the proposed cross-device synchronization framework for
privacy-preserving distributed machine learning.
epochs to track progression and stability through-
out the training phase.
5 Experiments
The results presented in Table 1showcase the per-
formance of various cross-device synchronization
techniques for distributed machine learning with a
focus on privacy constraints. Our proposed method
outperforms existing models in both communica-
tion latency and model accuracy.
Specifically, the proposed method achieves an
impressive communication latency of **175 ms**,
significantly lower than the other benchmarks,
which range from **180 ms** to **250 ms**. Ad-
ditionally, it attains a remarkable model accuracy
of **87.5%**, which is the highest among all com-
pared methods, surpassing the next closest method,
LOOP-MAC, by **2.5%**.
All methods were evaluated on a consistent
setup involving **50 devices** and a fixed learn-
ing rate of **
1×10−3
** along with a differen-
tial privacy noise multiplier of **0.1**. The im-
provements in both latency and accuracy indicate
that our technique not only enhances collabora-
tion among devices but also preserves data privacy
effectively. This exceptional performance empha-
sizes the potential of privacy-preserving synchro-
nization methodologies in real-world applications,
where both efficiency and confidentiality are cru-
cial.
5.1 Ablation Studies
To evaluate the impact of various components
within our proposed cross-device synchronization
framework, we conducted an ablation study focus-
ing on the individual contributions of critical el-
ements. We examined six configurations of the
system by selectively removing key components,
as described below:
•
Without Encryption: This configuration pro-
cesses local updates without any encryption pro-
tocol, leading to heightened vulnerability in data
confidentiality. As a result, the communication
latency is recorded at 250 ms, and the model
accuracy drops to 76.2%.
•
Without SMPC: In this case, the framework func-
tions without secure multi-party computation,
resulting in a communication latency of 210 ms
and a model accuracy of 82.0%. The absence of
this mechanism compromises the secure aggre-
gation of model updates.
•
Without Differential Privacy: This scenario indi-
cates that loss of differential privacy techniques
results in slightly improved latency at 230 ms,
but the model accuracy declines to 78.9%. The
lack of differential privacy exposes sensitive in-
dividual data points during training.
•
Without Aggregation: This configuration shows
the model updates being processed individually
rather than aggregated. The consequence is a
communication latency of 240 ms and an even
lower accuracy of 75.4%, illustrating the impor-
tance of aggregated updates in improving accu-
racy.
•
Without Multi-Device Support: In scenarios
where only a limited number of devices (10)
are permitted, we see a latency reduction down
to 200 ms; however, model accuracy settles at
80.0%. This highlights the advantages of scaling
to multiple devices for collaborative learning.
Our comprehensive results are presented in Ta-
ble 2, which indicates that the proposed full frame-
work, with all components intact, achieves a com-
munication latency of 175 ms and a model accuracy
of 87.5%. These figures emphasize the necessity
and effectiveness of incorporating encryption, se-
cure multi-party computation, differential privacy,
aggregation methodologies, and multi-device sup-
port into the synchronization framework. Each
component significantly contributes to maintaining
both privacy and model performance, illustrating
the synergy achieved through their integration in
Technique Computation Overhead (ms) EncryptionStrength Model Accuracy (%)
Secure Multi-Party Computation 50 High 86.0
Additive Homomorphic Encryption 70 Medium 84.5
Paillier Encryption 60 High 85.0
Elliptic Curve Cryptography 40 Low 82.7
Proposed Encryption Method 30 High 87.5
Table 3: Performance Metrics of Cryptographic Tech-
niques for Device Updates in Cross-Device Synchro-
nization
a privacy-preserving distributed machine learning
context.
5.2 Cryptographic Techniques for Device
Updates
Cryptographic techniques play a critical role in
ensuring confidentiality during cross-device syn-
chronization in distributed machine learning. The
performance metrics in Table 3highlight the ad-
vantages of various encryption methods used for
device updates.
The proposed encryption method demon-
strates superior efficiency and accuracy. With a
computation overhead of just 30 ms, this method
not only achieves the highest model accuracy at
87.5% but also maintains a high level of encryp-
tion strength. In comparison, Secure Multi-Party
Computation, while effective with high encryp-
tion strength, incurs a higher computational over-
head of 50 ms with a corresponding accuracy of
86.0%. Additive Homomorphic Encryption and
Paillier Encryption offer medium and high encryp-
tion strengths, respectively, but with slower compu-
tational speeds and slightly lower model accuracies
(84.5% and 85.0%).
Elliptic Curve Cryptography, although hav-
ing the lowest computation time of 40 ms, regis-
ters a significantly lower accuracy of 82.7% and
lower encryption strength.
The findings illustrate the trade-offs in selecting
cryptographic techniques for privacy-preserving
synchronization, emphasizing the vital balance be-
tween computational efficiency and model accu-
racy in distributed machine learning systems.
5.3 Local Update Computation and
Encryption
The efficiency of local update computation and en-
cryption in distributed machine learning is critical
for optimizing performance while ensuring privacy.
The experiments, as summarized in Table 4, reveal
various methods and their respective metrics.
Our proposed method demonstrates supe-
rior performance. In comparison to existing ap-
proaches, the proposed technique achieves a signifi-
cantly lower update computation time of 40 ms and
an encryption time of only 22 ms. Additionally, it
maintains an impressive accuracy drop of merely
1.0%, the lowest among all methods evaluated.
This highlights the effectiveness of our privacy-
preserving framework, particularly in the context
of distributed learning, where maintaining model
accuracy is essential. The significant improvements
in both computation and encryption times suggest
that our method can facilitate faster synchroniza-
tion without compromising data confidentiality.
The performance metrics of existing methods in-
dicate varied results. For instance, the LOOP-MAC
achieved the shortest update and encryption times
of 45 ms and 25 ms, respectively, with a minimal
accuracy drop of 1.8%. Conversely, the Certifica-
tion Scheme exhibited the longest update (60 ms)
and encryption times (40 ms) with the highest accu-
racy drop of 4.0%. This analysis illustrates a clear
relationship between the method employed and the
efficiency of local update computation and encryp-
tion, emphasizing the importance of optimizing
these processes in the context of privacy-oriented
distributed machine learning systems.
5.4 Server Aggregation without Decryption
In addressing the challenges of cross-device syn-
chronization in distributed machine learning, our
proposed framework emphasizes privacy con-
straints through advanced cryptographic tech-
niques. Each device performs local updates and
encrypts them before these updates are sent to a
central server for aggregation, thus ensuring con-
fidential handling of sensitive data. By applying
secure multi-party computation (SMPC) and dif-
ferential privacy mechanisms, our approach allows
devices to collaborate without compromising indi-
vidual data points.
The evaluation of server aggregation techniques
presented in Table 5illustrates the performance of
various methods. Our proposed method demon-
strates the highest model accuracy of 88.1%
while also achieving the fastest aggregation time
of 110 ms. This is a significant improvement com-
pared to other techniques such as Homomorphic
Aggregation, which achieved 87.0% accuracy but
with a longer aggregation time of 140 ms. The use
of homomorphic encryption variants (e.g., Addi-
tive Homomorphic and Paillier Encryption) also
shows promising results, yet they do not match the
Method Update Computation Time (ms) Encryption Time (ms) Accuracy Drop (%)
DASH 50 30 2.3
LOOP-MAC 45 25 1.8
Distributed Learning Review 55 35 3.0
Smart Skin Control 52 32 2.1
Certification Scheme 60 40 4.0
Proposed Method 40 22 1.0
Table 4: Local Update Computation and Encryption Performance Metrics.
Technique Model Accuracy (%) Encryption Type Aggregation Time(ms) DataSize (MB)
SMPC 86.2 Homomorphic Encryption 120 5
SecureSum 84.7 AdditiveHomomorphic 130 5
Homomorphic Aggregation 87.0 Fully Homomorphic 140 5
Encrypted Mean 85.4 Paillier Encryption 125 5
Proposed Method 88.1 SecureMulti-Party Computation 110 5
Table 5: Evaluation of Server Aggregation Techniques
without Decryption
efficiency or accuracy of our proposed solution.
This analysis reveals a clear balance between
model accuracy, encryption type, and aggregation
time, highlighting the effectiveness and practicality
of our synchronization techniques while maintain-
ing robust privacy. The consistent data size across
all techniques further emphasizes that performance
differences stem from the aggregation methods and
encryption strategies, making our framework a vi-
able choice for real-world applications where pri-
vacy and efficiency are critical.
5.5 Implementation of Secure Multi-Party
Computation
Implementation Execution Time (s) Security Level Model Accuracy (%) Scalability
Naive SMC 2.5 Low 81.0 Limited
Optimized SMC 1.7 Medium 84.0 Moderate
Asynchronous SMC 1.2 High 85.5 High
Batch SMC 1.5 Medium 83.5 Very High
Adaptive SMC 1.0 High 87.0 Very High
Table 6: Performance Metrics of Various Secure Multi-
Party Computation Implementations
The implementation of Secure Multi-Party Com-
putation (SMPC) techniques is crucial for enhanc-
ing privacy in cross-device synchronization for dis-
tributed machine learning. Our experiments assess
various SMPC implementations focusing on exe-
cution time, security level, model accuracy, and
scalability.
Table 6presents the performance metrics of dif-
ferent SMPC versions. Adaptive SMC emerges as
the most effective implementation. It achieves the
lowest execution time of 1.0 seconds while main-
taining a high security level and the best model ac-
curacy at 87.0%. This highlights the effectiveness
of adaptive strategies in optimizing both speed and
accuracy for privacy-preserving machine learning
tasks. In contrast, the Naive SMC method presents
the lowest performance across all metrics, indicat-
ing that basic implementations may not be suitable
for applications requiring robust privacy measures.
Optimized SMC and Batch SMC demonstrate
balanced performance with moderate to high scala-
bility and security levels, but they still lag behind
Asynchronous SMC in terms of execution speed
and accuracy. The results indicate that prioritizing
advanced SMPC strategies can lead to significant
improvements in maintaining private and effective
distributed learning systems.
6 Conclusions
This paper presents a framework for Cross-Device
Synchronization Techniques in Distributed Ma-
chine Learning that addresses privacy constraints.
The framework employs advanced cryptographic
methods, allowing devices to compute local up-
dates on their data and encrypt these updates be-
fore transmission to a central server. The aggrega-
tion of these encrypted updates is performed with-
out decryption, safeguarding sensitive information
throughout the synchronization. We incorporate
secure multi-party computation (SMPC) and dif-
ferential privacy concepts to enhance collaboration
while keeping individual data points confidential.
Experimental results indicate that our approach
maintains high model accuracy alongside strong
privacy protections. Furthermore, we explore the
balance between communication efficiency and pri-
vacy levels, offering guidance for optimizing dis-
tributed machine learning implementations. The
findings affirm the potential for privacy-preserving
synchronization to meet the demands of real-world
applications where data confidentiality is critical.
7 Limitations
Our framework has certain limitations that need to
be acknowledged. Firstly, while the use of crypto-
graphic techniques significantly enhances privacy,
it can also introduce a computational overhead that
may affect the efficiency of model updates, partic-
ularly in resource-constrained environments. Ad-
ditionally, the reliance on secure multi-party com-
putation (SMPC) means that the performance can
degrade in scenarios with high latency or network
instability, as the coordination among devices be-
comes crucial. Furthermore, implementing differ-
ential privacy mechanisms may result in a trade-off
between privacy guarantee and model accuracy,
which needs careful consideration in real-world
applications. Future research will focus on optimiz-
ing these trade-offs and enhancing the scalability
of cross-device synchronization techniques while
maintaining rigorous privacy standards.
References
Payam Abdisarabshali, Nicholas Accurso, Filippo Ma-
landra, Wei-Long Su, and Seyyedali Hosseinalipour.
2023. Synergies between federated learning and
o-ran: Towards an elastic virtualized architecture
for multiple distributed machine learning services.
ArXiv, abs/2305.02109.
Reem M. Alzhrani and Mohammed Alliheedi. 2023.
5g networks and iot devices: Mitigating ddos
attacks with deep learning techniques. ArXiv,
abs/2311.06938.
M. Anisetti, C. Ardagna, Nicola Bena, and Ernesto
Damiani. 2023. Towards certification of ma-
chine learning-based distributed systems. ArXiv,
abs/2305.16822.
Ioannis Arapakis, P. Papadopoulos, Kleomenis Katevas,
and Diego Perino. 2023. P4l: Privacy preserving
peer-to-peer learning for infrastructureless setups.
ArXiv, abs/2302.13438.
Amine Barrak, Fábio Petrillo, and Fehmi Jaafar. 2023.
Architecting peer-to-peer serverless distributed ma-
chine learning training for improved fault tolerance.
ArXiv, abs/2302.13995.
Ziqin Chen and Yongqiang Wang. 2024. Privacy-
preserving distributed optimization and learning.
ArXiv, abs/2403.00157.
Mohammad Dehghani and Zahra Yazdanparast. 2023.
A survey from distributed machine learning to dis-
tributed deep learning. ArXiv, abs/2307.05232.
Ron Dorfman, S. Vargaftik, Y. Ben-Itzhak, and K. Levy.
2023. Docofl: Downlink compression for cross-
device federated learning. pages 8356–8388.
I. Goodfellow, Mehdi Mirza, Xia Da, Aaron C.
Courville, and Yoshua Bengio. 2013. An empirical
investigation of catastrophic forgeting in gradient-
based neural networks. CoRR, abs/1312.6211.
Beomyeol Jeon, Linda Cai, Pallavi Srivastava, Jintao
Jiang, Xiaolan Ke, Yitao Meng, Cong Xie, and In-
dranil Gupta. 2020. Baechi: fast device placement
of machine learning graphs. In Proceedings of the
11th ACM Symposium on Cloud Computing, pages
416–430.
Yeonsoo Jeon, M. Erez, and Michael Orshansky.
2023. Artemis: He-aware training for effi-
cient privacy-preserving machine learning. ArXiv,
abs/2310.01664.
David Jin, Niclas Kannengießer, Sascha Rank, and
A. Sunyaev. 2023. A design toolbox for the devel-
opment of collaborative distributed machine learning
systems. ArXiv, abs/2309.16584.
Avetik G. Karagulyan, Egor Shulgin, Abdurakhmon
Sadiev, and Peter Richt’arik. 2024. Spam: Stochastic
proximal point method with momentum variance re-
duction for non-convex cross-device federated learn-
ing. ArXiv, abs/2405.20127.
Marc Katzef, Andrew C. Cullen, Tansu Alpcan,
C. Leckie, and Justin Kopacz. 2023. Failure-tolerant
distributed learning for anomaly detection in wireless
networks. ArXiv, abs/2303.13015.
Markus Wendelin Knauer, Maximilian Denninger, and
Rudolph Triebel. 2022. Recall: Rehearsal-free
continual learning for object classification. 2022
IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), pages 63–70.
Lanqing Li, Li Zeng, Zi-Chao Gao, Shen Yuan, Yatao
Bian, Bing Wu, Heng Zhang, Chan Lu, Yang Yu, Wei
Liu, Hongteng Xu, Jia Li, P. Zhao, and P. Heng. 2022.
Imdrug: A benchmark for deep imbalanced learning
in ai-aided drug discovery. ArXiv, abs/2209.07921.
Meiyi Li and Javad Mohammadi. 2023. Machine learn-
ing infused distributed optimization for coordinating
virtual power plant assets. ArXiv, abs/2310.17882.
Songqi Li. 2023. Smart skin separation control
using distributed-input distributed-output, multi-
modal actuators, and machine learning. ArXiv,
abs/2311.08116.
Seng Pei Liew, Satoshi Hasegawa, and Tsubasa Taka-
hashi. 2022. Shuffled check-in: Privacy amplifica-
tion towards practical distributed learning. ArXiv,
abs/2206.03151.
Fucai Luo, S. Al-Kuwari, Haiyan Wang, and Xingfu
Yan. 2023. Fssa: Efficient 3-round secure aggrega-
tion for privacy-preserving federated learning. ArXiv,
abs/2305.12950.
Hua Ma, Qun Li, Yifeng Zheng, Zhi Zhang, Xiaon-
ing Liu, Yan Gao, S. Al-Sarawi, and Derek Abbott.
2022. Mud-pqfed: Towards malicious user detection
in privacy-preserving quantized federated learning.
ArXiv, abs/2207.09080.
Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, and
G. Suh. 2023. Bounding the invertibility of privacy-
preserving instance encoding using fisher informa-
tion. ArXiv, abs/2305.04146.
A. Morales, Julian Fierrez, R. Vera-Rodríguez, and
Rubén Tolosana. 2019. Sensitivenets: Learning ag-
nostic representations with application to face images.
IEEE Transactions on Pattern Analysis and Machine
Intelligence, 43:2158–2164.
Haowei Ni, Shuchen Meng, Xieming Geng, Panfeng
Li, Zhuoying Li, Xupeng Chen, Xiaotong Wang, and
Shiyao Zhang. 2024. Time series modeling for heart
rate prediction: From arima to transformers.arXiv
preprint arXiv:2406.12199.
Sikha Pentyala, David Melanson, Martine De Cock, and
G. Farnadi. 2022a. Privfair: a library for privacy-
preserving fairness auditing. ArXiv, abs/2202.04058.
Sikha Pentyala, Nicola Neophytou, A. Nascimento, Mar-
tine De Cock, and G. Farnadi. 2022b. Privfairfl:
Privacy-preserving group fairness in federated learn-
ing. ArXiv, abs/2205.11584.
Y. Rahulamathavan, Charuka Herath, Xiaolan Liu,
S. Lambotharan, and C. Maple. 2023. Fhefl: Fully
homomorphic encryption friendly privacy-preserving
federated learning with byzantine users. ArXiv,
abs/2306.05112.
Tobias Ringwald and R. Stiefelhagen. 2021. Adaptiope:
A modern benchmark for unsupervised domain adap-
tation. 2021 IEEE Winter Conference on Applica-
tions of Computer Vision (WACV), pages 101–110.
Jonas Sander, Sebastian Berndt, Ida Bruhns, and
T. Eisenbarth. 2023. Dash: Accelerating distributed
private machine learning inference with arithmetic
garbled circuits. ArXiv, abs/2302.06361.
Esha Sarkar, E. Chielle, Gamze Gursoy, Leo Chen,
M. Gerstein, and Michail Maniatakos. 2022. Scal-
able privacy-preserving cancer type prediction with
homomorphic encryption. ArXiv, abs/2204.05496.
Efstathia Soufleri, Gobinda Saha, and Kaushik
Roy. 2022. Synthetic dataset generation for
privacy-preserving machine learning. ArXiv,
abs/2210.03205.
Omer Subasi, Oceane Bel, Joseph Manzano, and
Kevin J. Barker. 2023. The landscape of modern
machine learning: A review of machine, distributed
and federated learning. ArXiv, abs/2312.03120.
Sanku Satya Uday, Satti Thanuja Pavani, T. Lakshmi,
and Rohit Chivukula. 2022. Classifying human ac-
tivities using machine learning and deep learning
techniques. ArXiv, abs/2205.10325.
Hemanth Venkateswara, José Eusébio, Shayok
Chakraborty, and S. Panchanathan. 2017. Deep
hashing network for unsupervised domain adapta-
tion. 2017 IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), pages 5385–5394.
Kun Wang, Yi-Rui Yang, and Wu-Jun Li. 2024.
Buffered asynchronous secure aggregation for cross-
device federated learning. ArXiv, abs/2406.03516.
Kai Yi, Timur Kharisov, Igor Sokolov, and Peter
Richt’arik. 2024. Cohort squeeze: Beyond a sin-
gle communication round per cohort in cross-device
federated learning. ArXiv, abs/2406.01115.
J. Zalonis, Frederik Armknecht, Björn Grohmann, and
Manuel Koch. 2022. Report: State of the art solu-
tions for privacy preserving machine learning in the
medical context. ArXiv, abs/2201.11406.
