Secure and verifiable federated learning against poisoning attacks in IoMT

Federated learning applied to IoMT can effectively solve the problem of data silos in healthcare, improving healthcare quality and efficiency while ensuring the privacy protection of healthcare data. Since adversaries can track and derive clients’ privacy from the shared gradients, federated learning is still exposed to various security and privacy threats. In this paper, we consider two significant concerns regarding the federated learning poisoning attacks in IoMT: (1) how the medical cloud can verify the integrity of the local gradient and (2) how the medical client can verify the correctness of the aggregated results returned. To solve the above problems, we propose two federated learning data integrity verification schemes S-LMI and C-GMI. In the scheme, masking protocol is used to protect the privacy of gradient in federated learning. The homomorphic hash function is explored for both local gradient batch verification and global gradient aggregation verification, ensuring the gradient’s integrity. There is no need for data transmission between clients, which can not only protect client privacy but also reduce communication overhead. Our schemes are proved to satisfy unforgeability under the computational Diffie–Hellman problem. The theoretical analysis and extensive experimental results demonstrate that the schemes have high efficiency in the verification process compared to the existing schemes.