AAAI 2026technical0 citations
FedMerge: Federated Model Merging for Personalization
Shutong Chen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
Abstract
One global model in federated learning (FL) might not be sufficient to serve many clients with non-IID tasks and distributions. Despite recent advances in FL to train multiple global models for better personalization, they only provide limited model choices to clients, so local finetuning of multiple models is still indispensable. This paper proposes a novel ``FedMerge
BibTeX
@inproceedings{aaai2026_fedmergefederate,
title = {FedMerge: Federated Model Merging for Personalization},
author = {Shutong Chen and Tianyi Zhou and Guodong Long and Jing Jiang and Chengqi Zhang},
booktitle = {AAAI 2026},
year = {2026}
}