FedMeS: Personalized Federated Continual Learning Leveraging Local Memory

arXiv:2404.12710v1 Announce Type: new
Abstract: We focus on the problem of Personalized Federated Continual Learning (PFCL): a group of distributed clients, each with a sequence of local tasks on arbitrary data distributions, collaborate through a central server to train a personalized model at each client, with the model expected to achieve good performance on all local tasks. We propose a novel PFCL framework called Federated Memory Strengthening FedMeS to address the challenges of client drift and catastrophic forgetting. In FedMeS, …

abstract arxiv client continual cs.lg data distributed focus good memory personalized server tasks through train type