Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929392354066432 |
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| author | Neto, Afonso de Sá Delgado Egger, Maximilian Bakshi, Mayank Bitar, Rawad |
| author_facet | Neto, Afonso de Sá Delgado Egger, Maximilian Bakshi, Mayank Bitar, Rawad |
| contents | We introduce CYBER-0, the first zero-order optimization algorithm for memory-and-communication efficient Federated Learning, resilient to Byzantine faults. We show through extensive numerical experiments on the MNIST dataset and finetuning RoBERTa-Large that CYBER-0 outperforms state-of-the-art algorithms in terms of communication and memory efficiency while reaching similar accuracy. We provide theoretical guarantees on its convergence for convex loss functions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14362 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization Neto, Afonso de Sá Delgado Egger, Maximilian Bakshi, Mayank Bitar, Rawad Machine Learning Artificial Intelligence We introduce CYBER-0, the first zero-order optimization algorithm for memory-and-communication efficient Federated Learning, resilient to Byzantine faults. We show through extensive numerical experiments on the MNIST dataset and finetuning RoBERTa-Large that CYBER-0 outperforms state-of-the-art algorithms in terms of communication and memory efficiency while reaching similar accuracy. We provide theoretical guarantees on its convergence for convex loss functions. |
| title | Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.14362 |