1. Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm
- Author
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Elbakary, Ahmed, Issaid, Chaouki Ben, Shehab, Mohammad, Seddik, Karim, ElBatt, Tamer, and Bennis, Mehdi
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Computer Science - Distributed, Parallel, and Cluster Computing - Abstract
Federated learning is a machine learning approach where multiple devices collaboratively learn with the help of a parameter server by sharing only their local updates. While gradient-based optimization techniques are widely adopted in this domain, the curvature information that second-order methods exhibit is crucial to guide and speed up the convergence. This paper introduces a scalable second-order method, allowing the adoption of curvature information in federated large models. Our method, coined Fed-Sophia, combines a weighted moving average of the gradient with a clipping operation to find the descent direction. In addition to that, a lightweight estimation of the Hessian's diagonal is used to incorporate the curvature information. Numerical evaluation shows the superiority, robustness, and scalability of the proposed Fed-Sophia scheme compared to first and second-order baselines., Comment: ICC 2024
- Published
- 2024