On the Effect of Defections in Federated Learning and How to Prevent Them

• URL: http://arxiv.org/abs/2311.16459v1
• Date: Tue, 28 Nov 2023 03:34:22 GMT
• Title: On the Effect of Defections in Federated Learning and How to Prevent Them
• Authors: Minbiao Han, Kumar Kshitij Patel, Han Shao, Lingxiao Wang
• Abstract summary: Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model. This work demonstrates the impact of such defections on the final model's robustness and ability to generalize. We introduce a novel optimization algorithm with theoretical guarantees to prevent defections while ensuring convergence to demonstrate an effective solution for all participating agents.
• Score: 20.305263691102727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model. There are several federated learning applications where agents may choose to defect permanently$-$essentially withdrawing from the collaboration$-$if they are content with their instantaneous model in that round. This work demonstrates the detrimental impact of such defections on the final model's robustness and ability to generalize. We also show that current federated optimization algorithms fail to disincentivize these harmful defections. We introduce a novel optimization algorithm with theoretical guarantees to prevent defections while ensuring asymptotic convergence to an effective solution for all participating agents. We also provide numerical experiments to corroborate our findings and demonstrate the effectiveness of our algorithm.

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