Summary of Fedvck: Non-iid Robust and Communication-efficient Federated Learning Via Valuable Condensed Knowledge For Medical Image Analysis, by Guochen Yan et al.
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis
by Guochen Yan, Luyuan Xie, Xinyi Gao, Wentao Zhang, Qingni Shen, Yuejian Fang, Zhonghai Wu
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 federated learning method, Federated learning via Valuable Condensed Knowledge (FedVCK), addresses the challenges of heterogeneous data and non-independent and identical distribution (non-IID) in medical institutions. The approach condenses knowledge on the client side using latent distribution constraints, targeting high-quality information that has not been assimilated by the current model. This reduces unnecessary repetition and minimizes communication costs. On the server side, relational supervised contrastive learning provides more supervision signals for global model updating. Experimental results demonstrate FedVCK’s non-IID robustness and communication efficiency across various medical tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedVCK is a new way for hospitals to work together on important medical projects. Right now, these projects can be tricky because each hospital has different data and it’s hard to share that information without compromising patient privacy. The team behind FedVCK wants to solve this problem by finding the most important pieces of information from each hospital and sharing only those. This reduces the amount of data that needs to be shared, making it safer for patients. They also developed a new way for the hospitals’ computers to learn from each other, which helps them make better decisions. |
Keywords
» Artificial intelligence » Federated learning » Supervised
