Yu-Shi Liu

Communication-Efficient Federated Transfer Learning with Differential Privacy

Federated learning (FL) enables distributed clients to train a shared model without centralizing raw data, but the exchanged gradients or model updates can still leak private information through gradient inversion attacks. Differential privacy (DP) mitigates this risk by clipping client updates and injecting calibrated...