Summary of Personalized Multi-tier Federated Learning, by Sourasekhar Banerjee et al.
Personalized Multi-tier Federated Learning
by Sourasekhar Banerjee, Ali Dadras, Alp Yurtsever, Monowar Bhuyan
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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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 This research paper proposes a novel approach to personalized federated learning (PerFL) called PerMFL, which addresses the key challenge of capturing statistical heterogeneity properties with limited communication costs. PerMFL leverages multi-tier architecture and optimized local models for devices with known team structures. Theoretical guarantees are provided, showcasing linear convergence rates for smooth strongly convex problems and sub-linear rates for smooth non-convex issues. Numerical experiments demonstrate the robust performance of PerMFL, outperforming state-of-the-art methods in various personalized federated learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PerMFL is a new way to make sure different devices can work together to learn things without sharing too much information. This helps when devices have different kinds of data and we want to get the best results for each one. The paper shows that PerMFL works well and is better than other methods at doing this. |
Keywords
» Artificial intelligence » Federated learning
