Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2406.14362 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929392354066432 |
|---|---|
| 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 |