FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting
Multimodal data collected by heterogeneous devices are used for collaborative training, where federated learning (FL) serves as a key paradigm for effective distributed modeling with data privacy preservation. However, local training suffers from the forgetting of previously learned global knowledge under cross-client data heterogeneity, which leads to significant declines in both performance and convergence speed. Most previous studies rely on global alignment strategies to retain global knowledge, which hinder local optimization and lead to inadequate supervision of missing classes. Some studies introduce proxy datasets to supplement supervision for missing classes. However, it remains ...
| Authors | Zhenyan Liu, Hua Zhang, Haoran Gao et al. |
|---|---|
| Primary category | cs.CR |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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