Decentralized Federated Learning: A Survey on Security and Privacy
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909139947487232 |
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| author | Hallaji, Ehsan Razavi-Far, Roozbeh Saif, Mehrdad Wang, Boyu Yang, Qiang |
| author_facet | Hallaji, Ehsan Razavi-Far, Roozbeh Saif, Mehrdad Wang, Boyu Yang, Qiang |
| contents | Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new attack surfaces for malicious users of the network which may jeopardize the model performance and user and data privacy. For this reason, one of the main motivations for decentralized federated learning is to eliminate server-related threats by removing the server from the network and compensating for it through technologies such as blockchain. However, this advantage comes at the cost of challenging the system with new privacy threats. Thus, performing a thorough security analysis in this new paradigm is necessary. This survey studies possible variations of threats and adversaries in decentralized federated learning and overviews the potential defense mechanisms. Trustability and verifiability of decentralized federated learning are also considered in this study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17319 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decentralized Federated Learning: A Survey on Security and Privacy Hallaji, Ehsan Razavi-Far, Roozbeh Saif, Mehrdad Wang, Boyu Yang, Qiang Cryptography and Security Artificial Intelligence Machine Learning Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new attack surfaces for malicious users of the network which may jeopardize the model performance and user and data privacy. For this reason, one of the main motivations for decentralized federated learning is to eliminate server-related threats by removing the server from the network and compensating for it through technologies such as blockchain. However, this advantage comes at the cost of challenging the system with new privacy threats. Thus, performing a thorough security analysis in this new paradigm is necessary. This survey studies possible variations of threats and adversaries in decentralized federated learning and overviews the potential defense mechanisms. Trustability and verifiability of decentralized federated learning are also considered in this study. |
| title | Decentralized Federated Learning: A Survey on Security and Privacy |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2401.17319 |