Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm

• URL: http://arxiv.org/abs/2406.06655v1
• Date: Mon, 10 Jun 2024 09:57:30 GMT
• Title: Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm
• Authors: Ahmed Elbakary, Chaouki Ben Issaid, Mohammad Shehab, Karim Seddik, Tamer ElBatt, Mehdi Bennis,
• Abstract summary: 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.
• Score: 28.505671833986067
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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