Heterogeneous Federated Learning with Cross-layer Model Fusion.

ICCC(2023)

引用 0|浏览2
暂无评分
摘要
Federated Learning (FL) is a distributed machine learning framework that enables clients to train models using local datasets while preserving data privacy. However, FL faces challenges due to the complexity and diversity of data sources, computing resources, and client device capabilities, resulting in data and model heterogeneity. We propose an innovative FL framework called heterogeneous federated learning with cross-layer model fusion (HFedCMF) to address this. In this framework, clients upload their heterogeneous local models to the server, which then obtains a new global model using the cross-layer model fusion method that exploits the optimal transport properties. Additionally, we introduce a modified group lasso regularization in local training to separate the training of personalized models from the global fusion process, enhancing clients’ ability to achieve superior personalized performance. Experiments on various datasets demonstrate that the proposed HFedCMF outperforms homogeneous and heterogeneous FL algorithms.
更多
查看译文
关键词
Federated learning,model heterogeneity,non-i.i.d. data
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要