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Gang Du

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Book Open access Aug 2026

FedTail-DT: A Dual-Teacher Framework for Long-Tailed Heterogeneous FL with CLIP Prototypes and Adaptive Aggregation

Experiments show that FedTail-DT outperforms existing SOTA methods by 4.21% on CIFAR-10-LT and 3.14% on CIFAR-100-LT, demonstrating its effectiveness in handling both data heterogeneity and long-tailed problems.

Zijie Guo, Jinghua Zhu, Gang Du et al. · 0 citations
Book Open access Aug 2026

FedTail-DT: A Dual-Teacher Framework for Long-Tailed Heterogeneous FL with CLIP Prototypes and Adaptive Aggregation

Federated learning enables decentralized model training while preserving data privacy, showing significant potential in sensitive fields such as healthcare and finance. However, real-world client data often exhibits both heterogeneity and long-tailed label distributions, leading to class bias and knowledge gaps that li...

Zijie Guo, Jinghua Zhu, Gang Du et al. · 0 citations

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