2024/11/28 by Michael Cummins, Cummins, Michael, Guner Dilsad Er +3 · 1 voice · 1 citation
Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Privacy-Preserving Technologies in Data #cs.LG #math.OC
paper · pdf · doi:10.48550/arxiv.2411.19242
openalex publication_date 2024/11/28 · arxiv published 2024/11/28 · arxiv updated 2025/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We address the problem of client participation in federated learning, where traditional methods typically rely on a random selection of a small subset of clients for each training round. In contrast, we propose FedBack, a deterministic approach that leverages control-theoretic principles to manage client participation in ADMM-based federated learning. FedBack models client participation as a discrete-time dynamical system and employs an integral feedback controller to adjust each client's participation rate individually, based on the client's optimization dynamics. We provide global convergence guarantees for our approach by building on the recent federated learning research. Numerical experiments on federated image classification demonstrate that FedBack achieves up to 50% improvement in communication and computational efficiency over algorithms that rely on a random selection of clients.