Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2408.04963 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916632385814528 |
|---|---|
| author | Liu, Hong Shan, Liren Bao, Han You, Ronghui Yi, Yuhao Lv, Jiancheng |
| author_facet | Liu, Hong Shan, Liren Bao, Han You, Ronghui Yi, Yuhao Lv, Jiancheng |
| contents | Federated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This paper proposes an algorithmic framework for list-decodable federated learning, where a central server maintains a list of models, with at least one guaranteed to perform well. The framework has no strict restriction on the fraction of honest workers, extending the applicability of Byzantine federated learning to the scenario with more than half adversaries. Under proper assumptions on the loss function, we prove a convergence theorem for our method. Experimental results, including image classification tasks with both convex and non-convex losses, demonstrate that the proposed algorithm can withstand the malicious majority under various attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04963 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LiD-FL: Towards List-Decodable Federated Learning Liu, Hong Shan, Liren Bao, Han You, Ronghui Yi, Yuhao Lv, Jiancheng Machine Learning Federated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This paper proposes an algorithmic framework for list-decodable federated learning, where a central server maintains a list of models, with at least one guaranteed to perform well. The framework has no strict restriction on the fraction of honest workers, extending the applicability of Byzantine federated learning to the scenario with more than half adversaries. Under proper assumptions on the loss function, we prove a convergence theorem for our method. Experimental results, including image classification tasks with both convex and non-convex losses, demonstrate that the proposed algorithm can withstand the malicious majority under various attacks. |
| title | LiD-FL: Towards List-Decodable Federated Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.04963 |