Abstract: Federated learning in edge scenarios faces resource constraints, data heterogeneity, and security threats. This paper proposes a blockchain-based protection framework for personalized federated edge learning. The framework combines a decentralized consensus mechanism, secure compression, and personalized fine-tuning. These components jointly improve robustness to malicious clients and non-IID data. A coordination mechanism integrating secure compression and personalized fine-tuning is designed to enhance local model adaptability, reduce communication overhead, and ensure robust aggregation. A dual incentive mechanism combining contribution and trust is further proposed. Based on the Raft protocol, a verifiable random function is integrated to construct a consensus algorithm called VRF-VGRaft, enabling candidate node screening, random and verifiable leader election, and multi-party verification group construction. Simulation experiments were conducted on the FEMNIST and CIFAR10 datasets. The results show that, compared with existing methods, the framework significantly improves the security and efficiency of federated learning when handling non-IID data and malicious nodes.

Key words: blockchain, federated learning, consensus algorithm, edge computing, incentive mechanism