Decentralized Reinforced Anonymous FLchain: a Secure Federated Learning Architecture for the Medical Industry
Chenghan Wang, Shanshan Wang, Chuan Zhao, Wenyue Wang, Bin Hu, Youmian Wang, Lin Wang, Zhenxiang Chen · 2023
In the age of big data, data has already become a "high-value commodity" with clear price. Privacy leaks can lead to personal information security violations during training in federated learning, especially in the medical industry. The data of the medical industry is characterized by large amount of data and high demand for privacy. For this reason, we designed a Decentralized Reinforced Anonymous Federated Learning Based on Blockchain (DRA-FLchain) with high privacy protection and strong anonymity. In view of the large amount of data in the medical industry, DRA-FLchain uses blockchain technology to enable a large number of clients to participate in model training, and also uses cut through technology to reduce the cost of storage space. DRA-FLchain uses the blockchain and ring signature to ensure the anonymity of the client’s identity, and also uses homomorphic encryption and mask to protect the security of the model. For anonymous Federated Learning (FL), we set up a novel reward mechanism based on game theory Reward mechanism based on ring signature (RMBRS), which can distribute rewards fairly in the anonymous FL architecture. We compared the accuracy and operation efficiency of FL, Federated Learning Based on Blockchain (FLchain) and DRA-FLchain through experiments. The experimental results show that DRA-FLchain is an effective anonymous and secure architecture. Finally, we proved that DRA-FLchain can still protect the privacy of clients well in extreme cases through case study.