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arXiv Machine Learning By Sreejeet Maity, Aritra Mitra

Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning

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The paper introduces Robust Async-Fed-Q, an epoch‑based federated Q‑learning algorithm that combines variance‑reduced Bellman operator estimation with robust server aggregation. It provides high‑probability finite‑time guarantees showing that honest agents retain sample‑efficiency benefits even when some agents act adversarially, with the adversarial impact diminishing as honest data grows. The authors also present matching lower bounds, extend the framework to single‑trajectory sampling and heterogeneous coverage, and improve communication complexity for asynchronous federated Q‑learning.

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