Decentralized Federated Continual Learning Method Combined with Meta-learning
For the problems of continual learning and data security in federated continual scenarios,a decentralized federated con-tinual learning framework combined with meta-learning is constructed.First,in order to solve the problem of catastrophic forget-ting in incremental scenarios,an incremental meta-learning method based nearest mean-of-exemplars replaying called NMR-cMAML is proposed.Then,to solve the problem of privacy security in federated continual scenarios,a decentralized federated continual framework based on peer-to-peer network architecture is designed,which is different from the center architecture based on server-client.Each client in the decentralized framework adopts NMR-cMAML to learn the continuous tasks incrementally,and the effective knowledge migration between clients is realized by sharing the meta-learning model in the federal communication process.Finally,experiments are conducted on image data sets(Cifar100 and Imagenet50)to verify that the proposed method im-proves the data privacy security of the system and improves the local performance of the client.
万方数据
维普
