FAFedZO: Faster Zero-Order Adaptive Federated Learning Algorithm

Federated learning represents a newly emerging methodology in the field of machine learning that enables distributed agents to collaboratively learn a centralized model without sharing their raw data. Some scholars have already proposed many first-order algorithms and second-order algorithms for federated learning to reduce communication costs and speed up convergence. However, these algorithms generally rely on gradient or Hessian information, and we find it difficult to solve such federated optimization problems when the analytical expression of the loss function is not available, that is, when gradient information is not available. Therefore, we employed derivative-free federated zero-order optimization in this paper, which does not rely on specific gradient information, but instead utilizes the changes in function values or model outputs to estimate the optimization direction. Furthermore, to enhance the performance of derivative-free zero-order optimization, we propose an effective adaptive algorithm that can dynamically adjust the learning rate and other hyperparameters based on the performance during the optimization process, aiming to accelerate convergence. We rigorously analyze the convergence of our approach, and the experimental findings demonstrate our method can indeed achieve faster convergence speed on the MNIST, CIFAR-10 and Fashion-MNIST datasets in cases where gradient information is not available.