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cs.LG2024
Adaptive Guidance for Local Training in Heterogeneous Federated Learning
Jianqing Zhang, Yang Liu, Yang Hua +2
Model heterogeneity poses a significant challenge in Heterogeneous Federated Learning (HtFL). In scenarios with diverse model architectures, directly aggregating model parameters i…
cs.AI2024★ 2 cited
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
Jianqing Zhang, Yang Liu, Yang Hua +1
Heterogeneous Federated Learning (HtFL) enables task-specific knowledge sharing among clients with different model architectures while preserving privacy. Despite recent research p…
cs.LG2024★ 1 cited
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
Jianqing Zhang, Yang Liu, Yang Hua +1
Recently, Heterogeneous Federated Learning (HtFL) has attracted attention due to its ability to support heterogeneous models and data. To reduce the high communication cost of tran…