Nesterov-Accelerated Robust Federated Learning Over Byzantine Adversaries
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915597090029568 |
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| author | Xu, Lihan Dong, Yanjie Wang, Gang Zeng, Runhao Fan, Xiaoyi Hu, Xiping |
| author_facet | Xu, Lihan Dong, Yanjie Wang, Gang Zeng, Runhao Fan, Xiaoyi Hu, Xiping |
| contents | We investigate robust federated learning, where a group of workers collaboratively train a shared model under the orchestration of a central server in the presence of Byzantine adversaries capable of arbitrary and potentially malicious behaviors. To simultaneously enhance communication efficiency and robustness against such adversaries, we propose a Byzantine-resilient Nesterov-Accelerated Federated Learning (Byrd-NAFL) algorithm. Byrd-NAFL seamlessly integrates Nesterov's momentum into the federated learning process alongside Byzantine-resilient aggregation rules to achieve fast and safeguarding convergence against gradient corruption. We establish a finite-time convergence guarantee for Byrd-NAFL under non-convex and smooth loss functions with relaxed assumption on the aggregated gradients. Extensive numerical experiments validate the effectiveness of Byrd-NAFL and demonstrate the superiority over existing benchmarks in terms of convergence speed, accuracy, and resilience to diverse Byzantine attack strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nesterov-Accelerated Robust Federated Learning Over Byzantine Adversaries Xu, Lihan Dong, Yanjie Wang, Gang Zeng, Runhao Fan, Xiaoyi Hu, Xiping Machine Learning We investigate robust federated learning, where a group of workers collaboratively train a shared model under the orchestration of a central server in the presence of Byzantine adversaries capable of arbitrary and potentially malicious behaviors. To simultaneously enhance communication efficiency and robustness against such adversaries, we propose a Byzantine-resilient Nesterov-Accelerated Federated Learning (Byrd-NAFL) algorithm. Byrd-NAFL seamlessly integrates Nesterov's momentum into the federated learning process alongside Byzantine-resilient aggregation rules to achieve fast and safeguarding convergence against gradient corruption. We establish a finite-time convergence guarantee for Byrd-NAFL under non-convex and smooth loss functions with relaxed assumption on the aggregated gradients. Extensive numerical experiments validate the effectiveness of Byrd-NAFL and demonstrate the superiority over existing benchmarks in terms of convergence speed, accuracy, and resilience to diverse Byzantine attack strategies. |
| title | Nesterov-Accelerated Robust Federated Learning Over Byzantine Adversaries |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.02657 |