Summary of Fedstale: Leveraging Stale Client Updates in Federated Learning, by Angelo Rodio and Giovanni Neglia
FedStale: leveraging stale client updates in federated learning
by Angelo Rodio, Giovanni Neglia
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 federated learning algorithm, FedStale, to address the issue of heterogeneous client participation in federated learning. The algorithm aggregates model updates from participating clients and non-participating ones using a convex combination, adjusting the weight to balance between fresh and stale updates. This approach integrates and extends previous works on FedAvg and FedVARP to account for heterogeneous client participation. The analysis reveals that the least participating client’s data has the greatest influence on convergence error, while practical guidelines are provided to optimize the use of stale updates. Experimental results confirm the findings and demonstrate that FedStale outperforms both FedAvg and FedVARP in many scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedStale is a new algorithm for federated learning that helps when some clients don’t share their data as much as others. This happens because previous algorithms didn’t do well with this kind of unequal participation. The researchers created FedStale to solve this problem by combining fresh updates from participating clients and older updates from non-participating ones. They found out that the least participating client has a big impact on how well the algorithm works, and they gave tips on when to use old or new data. The experiments showed that FedStale is better than other algorithms in many situations. |
Keywords
» Artificial intelligence » Federated learning
