LoByITFL: Low Communication Secure and Private Federated Learning
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909647152087040 |
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| author | Xia, Yue Egger, Maximilian Hofmeister, Christoph Bitar, Rawad |
| author_facet | Xia, Yue Egger, Maximilian Hofmeister, Christoph Bitar, Rawad |
| contents | Privacy of the clients' data and security against Byzantine clients are key challenges in Federated Learning (FL). Existing solutions to joint privacy and security incur sacrifices on the privacy guarantee. We introduce LoByITFL, the first communication-efficient information-theoretically private and secure FL scheme that makes no sacrifices on the privacy guarantees while ensuring security against Byzantine adversaries. The key components are a small and representative dataset available to the federator, a careful modification of the FLTrust algorithm, and the one-time use of a trusted third party during an initialization period. We provide theoretical guarantees on the privacy and Byzantine resilience, as well as experimental results showing the convergence of LoByITFL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19217 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LoByITFL: Low Communication Secure and Private Federated Learning Xia, Yue Egger, Maximilian Hofmeister, Christoph Bitar, Rawad Information Theory Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning Privacy of the clients' data and security against Byzantine clients are key challenges in Federated Learning (FL). Existing solutions to joint privacy and security incur sacrifices on the privacy guarantee. We introduce LoByITFL, the first communication-efficient information-theoretically private and secure FL scheme that makes no sacrifices on the privacy guarantees while ensuring security against Byzantine adversaries. The key components are a small and representative dataset available to the federator, a careful modification of the FLTrust algorithm, and the one-time use of a trusted third party during an initialization period. We provide theoretical guarantees on the privacy and Byzantine resilience, as well as experimental results showing the convergence of LoByITFL. |
| title | LoByITFL: Low Communication Secure and Private Federated Learning |
| topic | Information Theory Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2405.19217 |