3 papers
cs.LG2026
When Privacy Hurts Mergeability: Geometry-Aware Model Merging under Differential Privacy
Jin Liu, Junkang Liu, Ning Xi +4
Model merging promises to construct a single multi-task model from independently fine-tuned task models without accessing the original task data. This makes it attractive when task…
cs.LG2026
DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models
Jin Liu, Yinbin Miao, Ning Xi +1
Balancing convergence efficiency and robustness under Differential Privacy (DP) is a central challenge in Federated Learning (FL). While AdamW accelerates training and fine-tuning…
cs.LG2026
Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
Jin Liu, Yinbin Miao, Ning Xi +1
Fine-tuning large vision models (LVMs) and large language models (LLMs) under differentially private federated learning (DPFL) is hindered by a fundamental privacy-utility trade-of…