区块链赋能多边缘安全联邦学习模型.

Federated learning is a revolutionary deep learning model, and it enables users to train the global model cooperatively without exposing their private data. However, some malicious clients can lead to the risk of single point of failure and privacy disclosure, this poses a serious threat to the security of federated learning. In response to the above issues, based on the existing research, this article proposed a blockchain empowered multi edge federated learning model. Firstly, this paper proposed to use blockchain instead of central server to enhance the stability and reliability of model training process; Secondly, this paper proposed a consensus mechanism based on edge computing to achieve a more efficient consensus process; In addition, incorporating reputation assessment into the federated learning training process can transparently measure the contribution value of each participant and standardize the behavior of work nodes. Finally, comparative experiments show that the scheme can maintain high accuracy in the malicious environment, and can resist higher malicious ratio compared with the traditional federated learning algorithm. [ABSTRACT FROM AUTHOR]