Abstract: Federated learning(FL) enables knowledge sharing among different clients by uploading and aggregating client modelswithout sharing data.However,existing FL methods generally assume that client data is known and fixed.In reality,clients continuously receive tasks with new category data and update their models,which leads to a continuous decline in model performance on old tasks,known as catastrophic forgetting.To address this severe challenge,researchers have introduced continual learning(CL) into FL,giving rise to the research direction of federated continual learning(FCL).Nevertheless,as the number of tasks received by clients increases,existing FCL methods become less effective in alleviating catastrophic forgetting,especially for tasks that are relatively distant in time,where accuracy drops significantly.Moreover,the increasing degree of data heterogeneity further weakens model accuracy.To address this issue,this paper proposes a local-global anti-forgetting mechanism to mitigate the forgetting problem on distant tasks.Specifically,it introduces task-specific lightweight modules at the client level to effectively overcome catastrophic forgetting caused by data changes and model updates.At the server level,it generates and filters category-balanced pseudo-images through model inversion to alleviate the decline in model performance due to data distribution diffe-rences.Through a series of experiments conducted on CIFAR-10,CIFAR-100,and TinyImageNet datasets,the results strongly demonstrate the superiority of the proposed mechanism.Compared with existing methods,it shows significant advantages in improving model performance and alleviating catastrophic forgetting.

Key words: Federated continual learning, Catastrophic forgetting, Data heterogeneity, Lightweight modules, Local-global anti forgetting