Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910905529270272 |
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| author | Lari, Ehsan Arablouei, Reza Gogineni, Vinay Chakravarthi Werner, Stefan |
| author_facet | Lari, Ehsan Arablouei, Reza Gogineni, Vinay Chakravarthi Werner, Stefan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13108 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing Lari, Ehsan Arablouei, Reza Gogineni, Vinay Chakravarthi Werner, Stefan Machine Learning Cryptography and Security Distributed, Parallel, and Cluster Computing Signal Processing 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. |
| title | Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing |
| topic | Machine Learning Cryptography and Security Distributed, Parallel, and Cluster Computing Signal Processing |
| url | https://arxiv.org/abs/2403.13108 |