Summary of Ranking-based Client Selection with Imitation Learning For Efficient Federated Learning, by Chunlin Tian et al.
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
by Chunlin Tian, Zhan Shi, Xinpeng Qin, Li Li, Chengzhong Xu
First submitted to arxiv on: 7 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 The proposed FedRank solution is an end-to-end, ranking-based approach for device selection in Federated Learning (FL). It tackles challenges like model performance, training efficiency, and data heterogeneity by viewing client selection as a ranking problem. The pairwise training strategy enables adaptive and efficient client choice, while imitation learning counters cold-start issues. Results show FedRank boosts accuracy by 5.2-56.9%, accelerates convergence up to 2.01x, and reduces energy consumption up to 40.1%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning helps devices train a shared model together while keeping data private. Choosing the right devices is crucial for good results and efficient training. The new FedRank approach solves this problem by ranking devices based on their ability to help with training. It’s like a competition where each device shows what it can do, and the best ones get chosen. This makes training faster and more accurate. Plus, it saves energy and works well even when some devices don’t have much data. |
Keywords
» Artificial intelligence » Federated learning
