Very few methods for hybrid federated learning, where clients only hold subsets of both features and samples, exist. Yet, this scenario is extremely important in practical settings. We provide a fast, robust algorithm for hybrid federated learning that hinges on Fenchel Duality. We prove the convergence of the algorithm to the same solution as if the model is trained centrally in a variety of practical regimes. Furthermore, we provide experimental results that demonstrate the performance improvements of the algorithm over a commonly used method in federated learning, FedAvg, and an existing hybrid FL algorithm, HyFEM. We also provide privacy considerations and necessary steps to protect client data.
@article{arxiv.2210.08106,
title = {A Primal-Dual Algorithm for Hybrid Federated Learning},
author = {Tom Overman and Garrett Blum and Diego Klabjan},
journal= {arXiv preprint arXiv:2210.08106},
year = {2025}
}
Comments
Accepted by AAAI 2024. To appear in AAAI proceedings