Summary of Decentralized Federated Policy Gradient with Byzantine Fault-tolerance and Provably Fast Convergence, by Philip Jordan et al.
Decentralized Federated Policy Gradient with Byzantine Fault-Tolerance and Provably Fast Convergence
by Philip Jordan, Florian Grötschla, Flint Xiaofeng Fan, Roger Wattenhofer
First submitted to arxiv on: 7 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to Federated Reinforcement Learning (FRL) that ensures fault-tolerance and eliminates the need for a trusted central agent. The method combines robust aggregation and Byzantine-resilient agreement techniques to enable decentralized FRL, improving upon existing approaches that lack guarantees against misbehaving agents or rely on a single point of failure. The proposed algorithm builds upon a centralized Byzantine fault-tolerant policy gradient (PG) method that outperforms previous methods under standard assumptions for non-fault-tolerant PG. The paper also provides the first sample complexity analysis for Byzantine fault-tolerant decentralized federated non-convex optimization, making it an important contribution in its own right. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research creates a way to help machines learn together without sharing their experiences or having a single leader. This is achieved by combining two techniques: one that makes sure the group stays on track and another that keeps the group safe from troublemakers. The new method builds upon an existing approach that works well even when some machines are not working correctly. This breakthrough can be used to make decisions in different environments, such as games or robot control. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
