Summary of Task-agnostic Federated Learning, by Zhengtao Yao et al.
Task-Agnostic Federated Learning
by Zhengtao Yao, Hong Nguyen, Ajitesh Srivastava, Jose Luis Ambite
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed self-supervised federated learning framework addresses task-agnostic and generalization problems in medical imaging, enabling effective representation learning across diverse datasets and tasks without requiring initial labels. By utilizing Vision Transformer (ViT) as the consensus feature encoder for pre-training, the approach adapts to unseen tasks and heterogeneous data distributions. Experimental evaluations on real-world non-IID medical imaging datasets demonstrate the framework’s efficacy, retaining 90% of F1 accuracy with only 5% of training data typically required for centralized approaches. The results indicate potential for federated learning architecture in multi-task foundation modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to share medical images while keeping personal information private. Doctors and researchers want to work together, but they need to make sure their data stays safe. This paper shows how to do that by using special computer algorithms to learn from lots of different medical image datasets without sharing the actual images. The new approach uses something called Vision Transformer (ViT) to help computers understand what’s important in each image. It works well even when the images are very different, which is great for doctors who need to diagnose and treat patients with different conditions. |
Keywords
» Artificial intelligence » Encoder » Federated learning » Generalization » Multi task » Representation learning » Self supervised » Vision transformer » Vit
