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.

FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation

A novel federated model soup method that optimizes the trade-off between local and global performance through selective interpolation of model parameters, alleviating overfitting and seeking flat minima for improved generalization.