pFedBlock : A Blockchain-Enhanced Split Federated Learning Framework for Robust and Traceable Model Training

Personalized federated learning is a practical solution to provide personalized services for Internet of Things devices while protecting their privacy. However, recent research found that personalized federated learning is still vulnerable to attacks, and its privacy can still be compromised during the training process. With an increasing number of devices and more diverse data, information traceability is also much more complicated. To address these problems, this paper proposes pFedBlock, a blockchain-based split federated learning framework to alleviate privacy risks and enhance the traceability during model training. Through the application of blockchain decentralized and immutable characteristics, pFedBlock can save each model update in a secure way and preserve a trustworthy log for all training behaviors. Such design can be beneficial to protect the training process and minimize the possible attacks from adversarial behaviors. Meanwhile, we also design a hybrid aggregation strategy in the federated framework so that devices can perform model updates in a more secure way. Experimental analysis shows that compared with traditional personalized federated learning methods, pFedBlock can obtain better performance in both models performance and system security.