Summary of Fedga: Federated Learning with Gradient Alignment For Error Asymmetry Mitigation, by Chenguang Xiao et al.
FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation
by Chenguang Xiao, Zheming Zuo, Shuo Wang
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
GrooveSquid.com Paper Summaries
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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) method called FedGA, which addresses the issue of intra-client and inter-client class imbalance in FL. The authors observe that conventional re-balancing methods fail to effectively address this bias, leading to biased client updates and deteriorating distributed models. To mitigate this issue, FedGA uses gradient alignment (GA) during the model backpropagation process, preventing catastrophic forgetting of rare classes and boosting model convergence and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For high school students or non-technical adults, Federated learning is a way for many devices to learn together without sharing their private data. This paper looks at how this process can be unfair because some devices have more data than others. The authors propose a new method called FedGA that helps make the learning process fairer and improves the accuracy of the results. |
Keywords
» Artificial intelligence » Alignment » Backpropagation » Boosting » Federated learning
