Swarm Federated Dimensionality Reduction Method Based on Correlation
Federated learning(FL)has significant advantages in solving the problems of privacy disclosure and data islands faced by artificial intelligence(AI).Previous studies on federated learning do not consider the problems of relevance and high dimensionality of data distributed among different federations.Based on the relevance of feder-ated data,a decentralized federated dimensionality reduction method is proposed.This method draws on the idea of Swarm learning(SL).Based on the separation of coupling features,a Swarm federated framework for canonical cor-relation analysis(CCA)is constructed to extract the low dimensional correlation features of Swarm nodes.In order to protect the privacy of collaboration parameters,a random disturbance strategy is also constructed to hide the privacy of Swarm features.Experiments on real data sets verify the effectiveness of the proposed method.
万方数据
维普
