Can Textual Gradient Work in Federated Learning?

We systematically explore the potential and challenges of incorporating textual gradient into Federated Learning, introducing FedTextGrad - a novel FL paradigm for optimizing LLMs.

Revisiting Delta-Parameter Pruning For Fine-Tuned Models

A comprehensive study of delta-parameter pruning that introduces DARq and AdamR to address the limitations of existing methods, enabling efficient storage and deployment of multiple fine-tuned models.

Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning

An innovative model interpolation-based local training technique that enhances local training across different clients through regularized model interpolation, acting as a catalyst for seamless adaptation of pre-trained models in federated learning.

Forgettable Federated Linear Learning with Certified Data Removal

A federated learning framework that enables certified data removal through linear approximation and efficient removal strategies, providing theoretical guarantees for the right to be forgotten.

Debiased Noise Editing on Foundation Models for Fair Medical Image Classification

A novel debiasing strategy that mitigates spurious correlations in medical images through noise editing, enabling fairness-aware image processing for both white-box and black-box foundation model APIs.