Personalized Federated Learning System Based on Permissioned Blockchain

Abstract

Federated learning ensures the privacy of data generated by large-scale IoT devices. Existing federated learning frameworks, based on centralized model coordinators, still face serious security challenges such as single point of failure and lack of privacy. In this paper, we propose a personalized federated learning system based on permissioned blockchain, which is divided into four layers of architecture are IoT device layer, network layer, edge computing layer, blockchain layer, and application layer, using permissioned blockchain as a federated learning server. And a permission blockchain-based personalized federation learning algorithm is proposed, which can achieve privacy protection and resistance to poisoning attacks with high accuracy. The experimental results revealed that the system has high privacy protection and anti-poisoning attack capability and can be deployed in edge computing situations.