Chongkai Li

FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

The proposed FedCVESA, a federated variant of Correlation Value Encoding Attack, is proposed by adding a Pearson-correlation regularizer to the loss function of target clients, so that private training data are gradually encoded into selected model parameters, referred to as carrier parameters.