FedAFD: Multimodal Federated Learning via Adversarial Fusion and Distillation
arXiv:2603.04890v1 Announce Type: new 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 re
arXiv:2603.04890v1 Announce Type: new 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 fused knowledge into the global model. Extensive experiments conducted under both IID and non-IID settings demonstrate that FedAFD achieves superior performance and efficiency for both the client and the server.
Executive Summary
The article proposes FedAFD, a novel multimodal federated learning framework that addresses the challenges of personalized client performance, modality/task discrepancies, and model heterogeneity. FedAFD introduces a bi-level adversarial alignment strategy and a granularity-aware fusion module to enhance client learning, and a similarity-guided ensemble distillation mechanism to handle model heterogeneity on the server side. The framework demonstrates superior performance and efficiency in both IID and non-IID settings, offering a promising solution for multimodal federated learning.
Key Points
- ▸ Introduction of a bi-level adversarial alignment strategy to align local and global representations
- ▸ Design of a granularity-aware fusion module to integrate global knowledge into personalized features
- ▸ Proposal of a similarity-guided ensemble distillation mechanism to handle model heterogeneity
Merits
Improved Personalized Performance
FedAFD's bi-level adversarial alignment strategy and granularity-aware fusion module enhance client performance by adapting to individual client needs
Demerits
Complexity of the Framework
The introduction of multiple components, such as adversarial alignment and ensemble distillation, may increase the complexity of the framework and require significant computational resources
Expert Commentary
The proposed FedAFD framework demonstrates a significant advancement in multimodal federated learning, addressing key challenges in the field. The bi-level adversarial alignment strategy and similarity-guided ensemble distillation mechanism are particularly noteworthy, as they enable the framework to adapt to individual client needs and handle model heterogeneity. However, the complexity of the framework may require careful consideration and optimization to ensure efficient deployment in real-world applications.
Recommendations
- ✓ Further research is needed to investigate the scalability and robustness of FedAFD in large-scale multimodal federated learning scenarios
- ✓ The development of simplified and efficient variants of FedAFD could facilitate wider adoption in practical applications