Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm
This addresses communication efficiency for federated learning systems, but it is incremental as it builds on existing second-order methods.
The paper tackled the problem of slow convergence in federated learning by introducing Fed-Sophia, a second-order algorithm that uses gradient moving averages and Hessian diagonal estimation, resulting in superior performance and scalability compared to baselines.
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.