Xiaoxue Zhang’s research while affiliated with University of Nevada, Reno and other places

Payment channel networks (PCNs) have been designed and utilized to address the scalability challenge and throughput limitation of blockchains. It provides a high-throughput solution for blockchain-based payment systems. However, such “layer-2” blockchain solutions have their own problems: payment channels require a separate deposit for each channel of two users. Thus it significantly locks funds from users into particular channels without the flexibility of moving these funds across channels. In this paper, we proposed Aggregated Payment Channel Network (APCN), in which flexible funds are used as a per-user basis instead of a per-channel basis. To prevent users from misbehaving such as double-spending, APCN includes mechanisms that make use of hardware trusted execution environments (TEEs) to control funds, balances, and payments. The distributed routing protocol in APCN also addresses the congestion problem to further improve resource utilization. Our prototype implementation and simulation results show that APCN achieves significant improvements on transaction success ratio with low routing latency, compared to even the most advanced PCN routing.

Decentralized federated learning (DFL) uses peer-to-peer communication to avoid the single point of failure problem in federated learning and has been considered an attractive solution for machine learning tasks on distributed devices. We provide the first solution to a fundamental network problem of DFL: what overlay network should DFL use to achieve fast training of highly accurate models, low communication, and decentralized construction and maintenance? Overlay topologies of DFL have been investigated, but no existing DFL topology includes decentralized protocols for network construction and topology maintenance. Without these protocols, DFL cannot run in practice. This work presents an overlay network, called FedLay, which provides fast training and low communication cost for practical DFL. FedLay is the first solution for constructing near-random regular topologies in a decentralized manner and maintaining the topologies under node joins and failures. Experiments based on prototype implementation and simulations show that FedLay achieves the fastest model convergence and highest accuracy on real datasets compared to existing DFL solutions while incurring small communication costs and being resilient to node joins and failures.

Quantum entanglement enables important computing applications such as quantum key distribution. Based on quantum entanglement, quantum networks are built to provide long-distance secret sharing between two remote communication parties. Establishing a multi-hop quantum entanglement exhibits a high failure rate, and existing quantum networks rely on trusted repeater nodes to transmit quantum bits. However, when the scale of a quantum network increases, it requires end-to-end multi-hop quantum entanglements in order to deliver secret bits without letting the repeaters know the secret bits. This work focuses on the entanglement routing problem, whose objective is to build long-distance entanglements via untrusted repeaters for concurrent source-destination pairs through multiple hops. Different from existing work that analyzes the traditional routing techniques on special network topologies, we present a comprehensive entanglement routing model that reflects the differences between quantum networks and classical networks as well as a new entanglement routing algorithm that utilizes the unique properties of quantum networks. Evaluation results show that the proposed algorithm Q-CAST increases the number of successful long-distance entanglements by a big margin compared to other methods. The model and simulator developed by this work may encourage more network researchers to study the entanglement routing problem.