Summary of An Aggregation-free Federated Learning For Tackling Data Heterogeneity, by Yuan Wang et al.
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
by Yuan Wang, Huazhu Fu, Renuga Kanagavelu, Qingsong Wei, Yong Liu, Rick Siow Mong Goh
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a novel Federated Learning (FL) algorithm called FedAF, which addresses the issue of client drift by avoiding the aggregation step altogether. Instead, clients collaborate to learn condensed data, and the server trains the global model using this condensed data and soft labels from the clients. This approach inherently avoids client drift, improves the quality of the condensed data, and enhances the performance of the global model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way of doing Federated Learning that helps computers learn together without sharing all their data. This is important because sometimes the data on different computers is very different, which can make it hard for them to work together. The new method, called FedAF, lets computers share what they’ve learned from each other’s data instead of sharing their raw data. This makes it easier for the computers to learn and helps them come up with a better overall model. |
Keywords
» Artificial intelligence » Federated learning
