About this document

Differential Privacy in Federated Learning by Sidhant Nair is a document available to read on EtoBox.

The paper presents a novel federated learning algorithm called DP-FedADMM that integrates differential privacy to enhance model convergence speed while maintaining privacy for local data. By addressing the slow convergence issue associated with traditional federated learning methods, DP-FedADMM outperforms existing algorithms like DP-FedAvg in terms of efficiency. The proposed method is validated through extensive experiments on various datasets, demonstrating its effectiveness in both privacy protection an

Author

Sidhant Nair

Language

EN