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UID:843@lincs.fr
DTSTART;TZID=Europe/Paris:20241018T110000
DTEND;TZID=Europe/Paris:20241018T120000
DTSTAMP:20241021T155529Z
URL:https://www.lincs.fr/events/decentralized-federated-policy-gradient-wi
 th-byzantine-fault-tolerance-and-provably-fast-convergence/
SUMMARY:Decentralized Federated Policy Gradient with Byzantine
 Fault-Tolerance and Provably Fast Convergence
DESCRIPTION:In Federated Reinforcement Learning (FRL)\, agents aim to
 collaboratively learn a common task\, while each agent is acting in its
 local environment without exchanging raw trajectories. Existing approaches
 for FRL either (a) do not provide any fault-tolerance guarantees (against
 misbehaving agents)\, or (b) rely on a trusted central agent (a single
 point of failure) for aggregating updates. We provide the first
 decentralized Byzantine fault-tolerant FRL method. Towards this end\, we
 first propose a new centralized Byzantine fault-tolerant policy gradient
 (PG) algorithm that improves over existing methods by relying only on
 assumptions standard for non-fault-tolerant PG. Then\, as our main
 contribution\, we show how a combination of robust aggregation and
 Byzantine-resilient agreement methods can be leveraged in order to
 eliminate the need for a trusted central entity. Since our results
 represent the first sample complexity analysis for Byzantine fault-tolerant
 decentralized federated non-convex optimization\, our technical
 contributions may be of independent interest. Finally\, we corroborate our
 theoretical results experimentally for common RL environments\,
 demonstrating the speed-up of decentralized federations w.r.t. the number
 of participating agents and resilience against various Byzantine
 attacks.\n\nPaper from Philip Jordan\, Florian Grötschla\, Flint Xiaofeng
 Fan and Roger Wattenhofer.
CATEGORIES:Practical Networks,Working Group,Youtube
LOCATION:Amphi 6\, 19 Place Marguerite Perey\, Palaiseau\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=19 Place Marguerite Perey\,
 Palaiseau\, France;X-APPLE-RADIUS=100;X-TITLE=Amphi 6:geo:0,0
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