Improving Group Connectivity for Generalization of Federated Deep Learning
<|reference_start|>Improving Group Connectivity for Generalization of Federated Deep Learning: Federated learning (FL) involves multiple heterogeneous clients collaboratively training a global model via iterative local updates and model fusion. The generalization of FL's global model has a large gap compared with centralized training, which is its bottleneck for broader applications. In this paper, we study and improve FL's generalization through a fundamental "connectivity'' perspective, which means how the local models are connected in the parameter region and fused into a generalized global model. The term "connectivity'' is derived from linear mode connectivity (LMC), studying the interpolated loss landscape of two different solutions (e.g., modes) of neural networks. Bridging the gap between LMC and FL, in this paper, we leverage fixed anchor models to empirically and theoretically study the transitivity property of connectivity from two models (LMC) to a group of models (model fusion in FL). Based on the findings, we propose FedGuCci(+), improving group connectivity for better generalization. It is shown that our methods can boost the generalization of FL under client heterogeneity across various tasks (4 CV datasets and 6 NLP datasets) and model architectures (e.g., ViTs and PLMs).<|reference_end|>
@article{li2024improving,
    title={Improving Group Connectivity for Generalization of Federated Deep
  Learning},
    author={Zexi Li, Jie Lin, Zhiqi Li, Didi Zhu, Rui Ye, Tao Shen, Tao Lin, Chao
  Wu},
    journal={arXiv preprint arXiv:2402.18949},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18949},
    primaryClass={cs.LG}
}