Siqi Shen

FedGS: Federated Graph-based Sampling with Arbitrary Client Availability

2022 · arXiv (Cornell University)

While federated learning has shown strong results in optimizing a machine learning model without direct access to the original data, its performance may be hindered by intermittent client availability which slows down the convergence and …

RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization

2023 · arXiv (Cornell University)

Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks. In the context of Multi-Agent Reinforcement Learning (MARL), learning coordinated and decentralized policies that are sensitive …