New algorithm tackles adversarial agents in federated reinforcement learning

Researchers have developed Robust Async-Fed-Q, a novel algorithm for federated reinforcement learning designed to maintain collaborative learning efficiency even when some agents act adversarially. This epoch-based method combines variance-reduced estimation at individual agents with robust aggregation at a central server. The algorithm provides theoretical guarantees showing that the benefits of collaboration are preserved among honest agents, with the impact of adversarial agents diminishing as data from honest agents increases, eventually vanishing in the infinite-sample limit. The work also establishes information-theoretic lower bounds, achieving nearly matching upper and lower bounds for adversarially robust federated reinforcement learning. AI

IMPACT This research could improve the robustness and efficiency of collaborative AI learning systems, particularly in scenarios with untrusted participants.

RANK_REASON This is a research paper detailing a new algorithm for federated reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Bellman optimality operator
• Federated Q-learning
• Markov decision process
• reinforcement learning
• Robust Async-Fed-Q

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