Summary of Fed-sophia: a Communication-efficient Second-order Federated Learning Algorithm, by Ahmed Elbakary et al.
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm
by Ahmed Elbakary, Chaouki Ben Issaid, Mohammad Shehab, Karim Seddik, Tamer ElBatt, Mehdi Bennis
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Federated learning enables collaborative model updates between devices and a parameter server without sharing raw data. While gradient-based methods dominate this space, incorporating curvature information from second-order techniques can significantly improve convergence rates. This paper presents Fed-Sophia, a scalable second-order method that combines weighted moving average gradients with clipping to find descent directions. Additionally, it employs lightweight Hessian diagonal estimation to incorporate curvature information. Experimental results demonstrate the superiority, robustness, and scalability of Fed-Sophia compared to first- and second-order baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way for devices to work together to learn from each other’s data without sharing their individual data. They use a new method that helps them learn more efficiently by considering the shape of the data they’re learning from. This method, called Fed-Sophia, is faster and more reliable than previous methods and can handle large models. The results show that this new approach outperforms others in terms of speed and reliability. |
Keywords
» Artificial intelligence » Federated learning
