arXiv:2605.28347v1 Announce Type: new Abstract: Multi-Label Recognition (MLR) based on Vision-Language Models (VLMs) aims to leverage their pre-trained knowledge to better adapt complex recognition scenarios, thereby enhancing model robustness. However, for realistic decentralized applications requiring federated learning, adapting VLMs to each client that possesses private and heterogeneous data can cause the model to overfit spurious label correlations, consequently triggering irrelevant categories when encountering new samples. To tackle this problem, we reconsider the federated learning fo
The rapid advancement and deployment of Vision-Language Models (VLMs) necessitate solutions for their secure and accurate implementation in privacy-sensitive, decentralized environments.
This research addresses fundamental challenges in federated learning for VLMs, specifically preventing overfitting to spurious correlations in private, heterogeneous datasets, which is crucial for robust and ethical AI deployment.
The development of FedMPT offers a pathway to more resilient and trustworthy multi-label recognition systems in decentralized AI applications, potentially broadening the applicability of VLMs in sensitive domains.
- · Privacy-sensitive industries (e.g., healthcare, finance)
- · Federated learning platforms
- · AI developers focused on ethical AI
- · Vision-Language Model researchers
- · Centralized model training paradigms
- · Organizations with weak data privacy standards
Improved accuracy and robustness of AI models in decentralized, data-private environments.
Increased adoption of federated learning for complex multi-modal AI tasks across various industries.
Enhanced trust in AI systems due to better handling of private data and reduced bias from spurious correlations, accelerating broader societal integration of AI.
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Read at arXiv cs.AI