FedControl: When Control Theory Meets Federated Learning

• URL: http://arxiv.org/abs/2205.14236v1
• Date: Fri, 27 May 2022 21:05:52 GMT
• Title: FedControl: When Control Theory Meets Federated Learning
• Authors: Adnan Ben Mansour, Gaia Carenini, Alexandre Duplessis and David Naccache
• Abstract summary: We distinguish client contributions according to the performance of local learning and its evolution. The technique is inspired from control theory and its classification performance is evaluated extensively in IID framework.
• Score: 63.96013144017572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To date, the most popular federated learning algorithms use coordinate-wise averaging of the model parameters. We depart from this approach by differentiating client contributions according to the performance of local learning and its evolution. The technique is inspired from control theory and its classification performance is evaluated extensively in IID framework and compared with FedAvg.

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