Summary of Mitigating Federated Learning Contribution Allocation Instability Through Randomized Aggregation, by Arno Geimer et al.
Mitigating federated learning contribution allocation instability through randomized aggregation
by Arno Geimer, Beltran Fiz, Radu State
First submitted to arxiv on: 13 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 A federated learning paradigm enables robust model development without sensitive data centralization. The challenge lies in fairly allocating participant contributions, as inaccurate allocation can erode trust, lead to unfair compensation, and reduce participation incentives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning lets many people work together on a big project without sharing their private information. It’s hard to make sure everyone gets the right credit for their part. If this is done badly, it might cause problems like not trusting each other or feeling unfairly rewarded. This could stop people from wanting to join in or help out. |
Keywords
» Artificial intelligence » Federated learning
