# Controlling Participation in Federated Learning with Feedback Source: [Controlling Participation in Federated Learning with Feedback]() Collection index: [llms.txt]() Data updated: 2025-10-25T18:16:09.851Z Authors: Michael Cummins, Guner Dilsad Er, Michael Muehlebach Published: 2024-11-28T16:26:13.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()