Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing

• URL: http://arxiv.org/abs/2403.13108v2
• Date: Fri, 16 Aug 2024 12:49:56 GMT
• Title: Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing
• Authors: Ehsan Lari, Reza Arablouei, Vinay Chakravarthi Gogineni, Stefan Werner,
• Abstract summary: Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. FL is vulnerable to model-poisoning attacks where malicious clients tamper with their local models to manipulate the global model. In this work, we investigate the resilience of the partial-sharing online FL (PSO-Fed) algorithm against such attacks.
• Score: 6.957420925496431
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) allows training machine learning models on distributed data without compromising privacy. However, FL is vulnerable to model-poisoning attacks where malicious clients tamper with their local models to manipulate the global model. In this work, we investigate the resilience of the partial-sharing online FL (PSO-Fed) algorithm against such attacks. PSO-Fed reduces communication overhead by allowing clients to share only a fraction of their model updates with the server. We demonstrate that this partial sharing mechanism has the added advantage of enhancing PSO-Fed's robustness to model-poisoning attacks. Through theoretical analysis, we show that PSO-Fed maintains convergence even under Byzantine attacks, where malicious clients inject noise into their updates. Furthermore, we derive a formula for PSO-Fed's mean square error, considering factors like stepsize, attack probability, and the number of malicious clients. Interestingly, we find a non-trivial optimal stepsize that maximizes PSO-Fed's resistance to these attacks. Extensive numerical experiments confirm our theoretical findings and showcase PSO-Fed's superior performance against model-poisoning attacks compared to other leading FL algorithms.

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