Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909471866880000 |
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| author | Egger, Maximilian Bakshi, Mayank Bitar, Rawad |
| author_facet | Egger, Maximilian Bakshi, Mayank Bitar, Rawad |
| contents | We introduce CyBeR-0, a Byzantine-resilient federated zero-order optimization method that is robust under Byzantine attacks and provides significant savings in uplink and downlink communication costs. We introduce transformed robust aggregation to give convergence guarantees for general non-convex objectives under client data heterogeneity. Empirical evaluations for standard learning tasks and fine-tuning large language models show that CyBeR-0 exhibits stable performance with only a few scalars per-round communication cost and reduced memory requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning Egger, Maximilian Bakshi, Mayank Bitar, Rawad Machine Learning Cryptography and Security Distributed, Parallel, and Cluster Computing We introduce CyBeR-0, a Byzantine-resilient federated zero-order optimization method that is robust under Byzantine attacks and provides significant savings in uplink and downlink communication costs. We introduce transformed robust aggregation to give convergence guarantees for general non-convex objectives under client data heterogeneity. Empirical evaluations for standard learning tasks and fine-tuning large language models show that CyBeR-0 exhibits stable performance with only a few scalars per-round communication cost and reduced memory requirements. |
| title | Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning |
| topic | Machine Learning Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2502.00193 |