Privacy-Preserving Federated Learning Against Label-Flipping Attacks on Non-IID Data.

A Split-and-Privatize Framework for Large Language Model Fine-Tuning.

Privacy-Preserving Distributed Estimation Over Multitask Networks.

Performance-Enhanced Federated Learning With Differential Privacy for Internet of Things.

Distributed Differential Utility/Cost Analysis for Privacy Protection.