Summary of Neighborhood and Global Perturbations Supported Sam in Federated Learning: From Local Tweaks to Global Awareness, by Boyuan Li et al.
Neighborhood and Global Perturbations Supported SAM in Federated Learning: From Local Tweaks To Global Awareness
by Boyuan Li, Zihao Peng, Yafei Li, Mingliang Xu, Shengbo Chen, Baofeng Ji, Cong Shen
First submitted to arxiv on: 26 Aug 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 FedTOGA algorithm is designed to enhance consistency between local and global generalization and optimization objectives in Federated Learning (FL) while maintaining minimal uplink communication overhead. By linking local perturbations to global updates, global generalization consistency is improved. Additionally, global updates are used to correct local dynamic regularizers, reducing bias and enhancing optimization consistency. The algorithm achieves faster convergence under non-convex functions, outperforming state-of-the-art algorithms with a 1% accuracy increase and 30% faster convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to make machine learning models work better together without sharing their data. This is important because it helps keep people’s private information safe while still making sure the model gets more accurate. The new method, called FedTOGA, makes sure that all the different pieces of the model are working well together and not getting stuck in a bad place. It does this by updating the model based on how well it’s doing at each step, which helps it get better faster. |
Keywords
» Artificial intelligence » Federated learning » Generalization » Machine learning » Optimization
