Summary of Federated Ucbvi: Communication-efficient Federated Regret Minimization with Heterogeneous Agents, by Safwan Labbi et al.
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents
by Safwan Labbi, Daniil Tiapkin, Lorenzo Mancini, Paul Mangold, Eric Moulines
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Federated Upper Confidence Bound Value Iteration algorithm (Fed-UCBVI) is a novel extension of the UCBVI algorithm tailored for the federated learning framework. This paper proves that Fed-UCBVI’s regret scales as O(sqrt(H^3 |S| |A| T / M)) with an additional term due to heterogeneity, where H is episode length, S is number of states, A is number of actions, M is number of agents, and T is number of episodes. In the single-agent setting, this upper bound matches the minimax lower bound up to polylogarithmic factors, while in the multi-agent scenario, Fed-UCBVI has linear speed-up. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new algorithm for learning with multiple agents, called Federated Upper Confidence Bound Value Iteration (Fed-UCBVI). This algorithm helps agents learn together without sharing their data. The authors show that this algorithm works well and is more efficient than other algorithms in some cases. They also introduce a new way to measure how different the agents’ environments are. |
Keywords
» Artificial intelligence » Federated learning
