@article{zhou_yu_li_gu_wang_2022, title={FedAegis: Edge-Based Byzantine-Robust Federated Learning for Heterogeneous Data}, ISSN={["2576-6813"]}, DOI={10.1109/GLOBECOM48099.2022.10000981}, journal={2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022)}, author={Zhou, Fangtong and Yu, Ruozhou and Li, Zhouyu and Gu, Huayue and Wang, Xiaojian}, year={2022}, pages={3005–3010} }
 @article{wang_gu_li_zhou_yu_yang_2022, title={Why Riding the Lightning? Equilibrium Analysis for Payment Hub Pricing}, ISSN={["1550-3607"]}, DOI={10.1109/ICC45855.2022.9839171}, abstractNote={Payment Channel Network (PCN) is an auspicious solution to the scalability issue of the blockchain, improving transaction throughput without relying on on-chain transactions. In a PCN, nodes can set prices for forwarding payments on behalf of other nodes, which motivates participation and improves network stability. Analyzing the price setting behaviors of PCN nodes plays a key role in understanding the economic properties of PCNs, but has been under-studied in the literature. In this paper, we apply equilibrium analysis to the price-setting game between two payment hubs in the PCN with limited channel capacities and partial overlap demand. We analyze existence of pure Nash Equilibriums (NEs) and bounds on the equilibrium revenue under various cases, and propose an algorithm to find all pure NEs. Using real data, we show bounds on the price of anarchy/stability and average transaction fee under realistic network conditions, and draw conclusions on the economic advantage of the PCN for making payment transfers by cryptocurrency users.}, journal={IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022)}, author={Wang, Xiaojian and Gu, Huayue and Li, Zhouyu and Zhou, Fangtong and Yu, Ruozhou and Yang, Dejun}, year={2022}, pages={5409–5414} }
 @article{yu_lo_zhou_xue_2021, title={Data-Driven Edge Resource Provisioning for Inter-Dependent Microservices with Dynamic Load}, ISSN={["2576-6813"]}, DOI={10.1109/GLOBECOM46510.2021.9685155}, abstractNote={This paper studies how to provision edge computing and network resources for complex microservice-based applications (MSAs) in face of uncertain and dynamic geo-distributed demands. The complex inter-dependencies between distributed microservice components make load balancing for MSAs extremely challenging, and the dynamic geo-distributed demands exacerbate load imbalance and consequently congestion and performance loss. In this paper, we develop an edge resource provisioning model that accurately captures the inter-dependencies between microservices and their impact on load balancing across both computation and communication resources. We also propose a robust formulation that employs explicit risk estimation and optimization to hedge against potential worst-case load fluctuations, with controlled robustness-resource trade-off. Utilizing a data-driven approach, we provide a solution that provides risk estimation with measurement data of past load geo-distributions. Simulations with real-world datasets have validated that our solution provides the important robustness crucially needed in MSAs, and performs superiorly compared to baselines that neglect either network or inter-dependency constraints.}, journal={2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)}, author={Yu, Ruozhou and Lo, Szu-Yu and Zhou, Fangtong and Xue, Guoliang}, year={2021} }