MS-FL: A Federated Learning Framework Based on Multiple Security Strategies

With the development of data science, AI and data transaction, an increasing number of users are utilizing multi-party data for federated machine learning to obtain their desired models. Therefore, scholars have proposed numerous federated learning frameworks to address practical issues. However, there are still three issues that need to be addressed in current federated learning frameworks: 1) privacy protection, 2) poisoning attack, and 3) protection of the interests of participants. To address these issues, this paper proposes a novel federated learning framework MS-FL based on multiple security strategies. The framework’s algorithms guarantee that data providers need not worry about data privacy leakage. At the same time, it can defend against poisoning attack from malicious nodes. Finally, to ensure the interests of all parties are protected, a blockchain protocol is utilized. The theoretical derivation proves the effectiveness of this framework. Experimental results show that the algorithm designed in this paper outperforms other algorithms.