FedAFD Enables Strong Cloud and Edge Performance in Multimodal Federated Learning

FedAFD, a CVPR 2026 paper, combines bi‑level adversarial alignment, granularity‑aware feature fusion, and similarity‑guided ensemble distillation to simultaneously boost cloud‑side global models and edge‑side personalization in multimodal federated learning, achieving state‑of‑the‑art results with only 20 communication rounds.

Adversarial AlignmentCVPR 2026Ensemble Distillation

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FedAFD Enables Strong Cloud and Edge Performance in Multimodal Federated Learning