【Author】
Wang, Naiyu; Yang, Wenti; Guan, Zhitao; Du, Xiaojiang; Guizani, Mohsen
【Source】2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
【Abstract】Federated Learning (FL), which allows multiple participants to co-train machine Learning models without exposing local data, has been recognized as a promising method in the past few years. However, in the FL process, the server side may steal sensitive information of users, while the client side may also upload malicious data to compromise the training of the global model. Most existing privacy-preservation FL schemes seldom deal with threats from both of these two sides at the same time. In this paper, we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL, which uses blockchain as the underlying distributed framework of FL. Homomorphic encryption and Multi-Krum technology are combined to achieve ciphertext-level model aggregation and model filtering, which can guarantee the verifiability of local models while realizing privacy-preservation. Security analysis and performance evaluation prove that the proposed scheme can achieve enhanced security and improve the performance of the FL model.
【Keywords】Federated Learning; Blockchain; Privacy-Preservation; Homomorphic Encryption
【标题】BPFL：基于区块链的隐私保护联邦学习方案
【摘要】联邦学习 (FL) 允许多个参与者在不暴露本地数据的情况下共同训练机器学习模型，在过去几年中被认为是一种很有前途的方法。但是，在FL过程中，服务器端可能会窃取用户的敏感信息，而客户端也可能会上传恶意数据，危及全局模型的训练。大多数现有的隐私保护 FL 方案很少同时处理来自这两个方面的威胁。在本文中，我们提出了一种名为 BPFL 的基于区块链的隐私保护联邦学习方案，该方案使用区块链作为 FL 的底层分布式框架。同态加密和Multi-Krum技术相结合，实现密文级模型聚合和模型过滤，在保证局部模型可验证性的同时实现隐私保护。安全性分析和性能评估证明，所提出的方案可以实现增强的安全性并提高 FL 模型的性能。
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