Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity

no code implementations • 31 May 2024 • Zheng Wang, Zhaopeng Peng, Zihui Wang, Cheng Wang

Finally, we facilitate the implicit aggregation of personal parameters by aggregating client embeddings through a self-attention module.

Personalized Federated Learning

FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning

no code implementations • 28 May 2024 • Zihui Wang, Zheng Wang, Lingjuan Lyu, Zhaopeng Peng, Zhicheng Yang, Chenglu Wen, Rongshan Yu, Cheng Wang, Xiaoliang Fan

Second, to implement the BCF, we design a submodel allocation module with a theoretical guarantee of fairness.

Fairness Federated Learning

FedPFT: Federated Proxy Fine-Tuning of Foundation Models

1 code implementation • 17 Apr 2024 • Zhaopeng Peng, Xiaoliang Fan, Yufan Chen, Zheng Wang, Shirui Pan, Chenglu Wen, Ruisheng Zhang, Cheng Wang

Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs.

Federated Learning

FLGo: A Fully Customizable Federated Learning Platform

no code implementations • 21 Jun 2023 • Zheng Wang, Xiaoliang Fan, Zhaopeng Peng, Xueheng Li, Ziqi Yang, Mingkuan Feng, Zhicheng Yang, Xiao Liu, Cheng Wang

Federated learning (FL) has found numerous applications in healthcare, finance, and IoT scenarios.

Federated Learning

Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting

no code implementations • 25 Nov 2021 • Chuanpan Zheng, Xiaoliang Fan, Shirui Pan, Haibing Jin, Zhaopeng Peng, Zonghan Wu, Cheng Wang, Philip S. Yu

However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks.