[2603.04890] FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation

Computer Science > Machine Learning arXiv:2603.04890 (cs) [Submitted on 5 Mar 2026] Title:FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation Authors:Min Tan, Junchao Ma, Yinfu Feng, Jiajun Ding, Wenwen Pan, Tingting Han, Qian Zheng, Zhenzhong Kuang, Zhou Yu View a PDF of the paper titled FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation, by Min Tan and 8 other authors View PDF HTML (experimental) Abstract: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 design a granularity-aware fusion module to integrate global knowledge into the personalized features adaptively. On the server side, to handle model heterogeneity, we propose a similarity-guided ensemble distillation mechanism that aggregates client representations on shared public data based on feature similarity and distills the f...