Summary of Emulating Full Client Participation: a Long-term Client Selection Strategy For Federated Learning, by Qingming Li et al.
Emulating Full Client Participation: A Long-Term Client Selection Strategy for Federated Learning
by Qingming Li, Juzheng Miao, Puning Zhao, Li Zhou, Shouling Ji, Bowen Zhou, Furui Liu
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 novel client selection strategy is proposed for federated learning, which emulates the performance achieved with full client participation. The approach minimizes the gradient-space estimation error between the client subset and the full client set in a single round, and introduces an individual fairness constraint to ensure similar frequencies of being selected for clients with similar data distributions. Lyapunov optimization and submodular functions are employed to efficiently identify the optimal subset of clients, and theoretical analysis is provided for convergence ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning helps multiple devices learn from each other’s data without sharing it directly. One challenge is choosing which devices should participate in the learning process. The proposed method chooses devices by minimizing the difference between how well a group of devices learns and how well all devices would learn if they all participated. This ensures that the chosen devices are representative of the overall group. Additionally, the method tries to make sure each device has an equal chance of being selected based on its data similarity to other devices. |
Keywords
» Artificial intelligence » Federated learning » Optimization
