High-Performance Personalized Federated Continual Learning Framework for Dynamic Data Sharing in IoV

The Internet of Vehicles (IoV) connects vehicles to the internet, delivering smarter and more convenient mobility services to users. Traditional intelligent service models typically employ federated learning (FL) for training. However, as vehicles continuously encounter new data and exhibit heterogeneous computational capabilities, these models often suffer from catastrophic forgetting and performance degradation. To address the above challenges, this paper focuses on training a personalized federated continual learning (PFCL) model that is expected to achieve good performance on all local tasks. The new PFCL scheme, called adaptive personalized federated continual learning (Adp-PFCL), handles catastrophic forgetting and the lack of personalized solutions. In Adp-PFCL, 1) each vehicle client uses the online continual learning (OCL) algorithm to train its local model in the local training stage to mitigate the forgetting of old knowledge; 2) during the local model update phase, each vehicle client locally executes the privacy-preserving federated deep mutual learning (PP-FedDML) strategy to facilitate personalization and improve the performance of the global model on local data. Extensive experimental results demonstrate that the Adp-PFCL scheme achieves at least a 28.36% improvement in average accuracy while reducing runtime by 7.75% compared with current mainstream approaches, validating its high adaptability to dynamic data and computational efficiency.