Summary of Regularizing and Aggregating Clients with Class Distribution For Personalized Federated Learning, by Gyuejeong Lee and Daeyoung Choi
Regularizing and Aggregating Clients with Class Distribution for Personalized Federated Learning
by Gyuejeong Lee, Daeyoung Choi
First submitted to arxiv on: 12 Jun 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 Class-wise Federated Averaging (cwFedAVG) method for personalized federated learning (PFL) addresses the limitations of existing PFL methods by reducing computational and communication costs. cwFedAVG performs Federated Averaging class-wise, creating multiple global models per class on the server. Local models integrate these global models weighted by their estimated local class distribution, derived from L2-norms of deep network weights. The Weight Distribution Regularizer (WDR) is designed to enhance accuracy in estimating local class distributions. Experimental results show that cwFedAVG matches or outperforms existing PFL methods while being computationally efficient and conceptually simple. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Personalized federated learning helps create customized models for people with different data. But current ways of doing this can be slow and use too much computing power. This paper introduces a new approach called Class-wise Federated Averaging (cwFedAVG). It works by creating multiple models for each class on the server, and then combining these models based on how well they fit the local data. To make it even better, the authors added a special tool that helps estimate how good each model is. The results show that cwFedAVG performs just as well as other methods but uses less computing power. |
Keywords
» Artificial intelligence » Federated learning
