FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation
Multimodal Federated Learning (MFL) enables clients with heterogeneous data modalities to collaboratively train models without sharing raw data, offering a privacy-preserving framework that leverages complementary cross-modal information. However, existing methods often overlook personalized client performance and struggle with modality/task discrepancies, as well as model heterogeneity. To address these challenges, we propose FedAFD, a unified MFL framework that enhances client and server learning. On the client side, we introduce a bi-level adversarial alignment strategy to align local and global representations within and across modalities, mitigating modality and task gaps. We further...
| Authors | Min Tan, Junchao Ma, Yinfu Feng et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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