FRIDA: Free-Rider Detection using Privacy Attacks
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914046556504064 |
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| author | Recasens, Pol G. Horváth, Ádám Gutierrez-Torre, Alberto Torres, Jordi Berral, Josep Ll. Pejó, Balázs |
| author_facet | Recasens, Pol G. Horváth, Ádám Gutierrez-Torre, Alberto Torres, Jordi Berral, Josep Ll. Pejó, Balázs |
| contents | Federated learning is increasingly popular as it enables multiple parties with limited datasets and resources to train a machine learning model collaboratively. However, similar to other collaborative systems, federated learning is vulnerable to free-riders - participants who benefit from the global model without contributing. Free-riders compromise the integrity of the learning process and slow down the convergence of the global model, resulting in increased costs for honest participants. To address this challenge, we propose FRIDA: free-rider detection using privacy attacks. Instead of focusing on implicit effects of free-riding, FRIDA utilizes membership and property inference attacks to directly infer evidence of genuine client training. Our extensive evaluation demonstrates that FRIDA is effective across a wide range of scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05020 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FRIDA: Free-Rider Detection using Privacy Attacks Recasens, Pol G. Horváth, Ádám Gutierrez-Torre, Alberto Torres, Jordi Berral, Josep Ll. Pejó, Balázs Machine Learning Cryptography and Security Federated learning is increasingly popular as it enables multiple parties with limited datasets and resources to train a machine learning model collaboratively. However, similar to other collaborative systems, federated learning is vulnerable to free-riders - participants who benefit from the global model without contributing. Free-riders compromise the integrity of the learning process and slow down the convergence of the global model, resulting in increased costs for honest participants. To address this challenge, we propose FRIDA: free-rider detection using privacy attacks. Instead of focusing on implicit effects of free-riding, FRIDA utilizes membership and property inference attacks to directly infer evidence of genuine client training. Our extensive evaluation demonstrates that FRIDA is effective across a wide range of scenarios. |
| title | FRIDA: Free-Rider Detection using Privacy Attacks |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2410.05020 |