Task-Agnostic Federated Learning
<|reference_start|>Task-Agnostic Federated Learning: In the realm of medical imaging, leveraging large-scale datasets from various institutions is crucial for developing precise deep learning models, yet privacy concerns frequently impede data sharing. federated learning (FL) emerges as a prominent solution for preserving privacy while facilitating collaborative learning. However, its application in real-world scenarios faces several obstacles, such as task & data heterogeneity, label scarcity, non-identically distributed (non-IID) data, computational vaiation, etc. In real-world, medical institutions may not want to disclose their tasks to FL server and generalization challenge of out-of-network institutions with un-seen task want to join the on-going federated system. This study address task-agnostic and generalization problem on un-seen tasks by adapting self-supervised FL framework. Utilizing Vision Transformer (ViT) as consensus feature encoder for self-supervised pre-training, no initial labels required, the framework enabling effective representation learning across diverse datasets and tasks. Our extensive evaluations, using various real-world non-IID medical imaging datasets, validate our approach's efficacy, retaining 90\% of F1 accuracy with only 5\% of the training data typically required for centralized approaches and exhibiting superior adaptability to out-of-distribution task. The result indicate that federated learning architecture can be a potential approach toward multi-task foundation modeling.<|reference_end|>
@article{yao2024task-agnostic,
    title={Task-Agnostic Federated Learning},
    author={Zhengtao Yao, Hong Nguyen, Ajitesh Srivastava, Jose Luis Ambite},
    journal={arXiv preprint arXiv:2406.17235},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17235},
    primaryClass={cs.CV cs.AI cs.DC}
}