Summary of Splitfedzip: Learned Compression For Data Transfer Reduction in Split-federated Learning, by Chamani Shiranthika et al.
SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning
by Chamani Shiranthika, Hadi Hadizadeh, Parvaneh Saeedi, Ivan V. Bajić
First submitted to arxiv on: 18 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 A novel Federated Learning approach, Split-Federated (SplitFed) learning, combines the strengths of Federated and Split Learning. SplitFed minimizes computational burdens by balancing computations across clients and servers, ensuring data privacy. This framework is ideal for various domains, particularly healthcare where data privacy is crucial. However, SplitFed networks face communication challenges like latency, bandwidth constraints, synchronization overhead, and large data transfers during learning. To address these issues, this paper proposes SplitFedZip, a method employing learned compression to reduce data transfer in SplitFed learning. Experimental results on medical image segmentation demonstrate that learned compression can significantly reduce data transmission while maintaining model accuracy. The implementation is available at https://github.com/ChamaniS/SplitFedZip. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SplitFed learning combines Federated and Split Learning to train a collaborative model without sharing local data. This approach minimizes computational burdens by balancing computations across clients and servers, ensuring data privacy. SplitFed networks encounter communication challenges like latency, bandwidth constraints, synchronization overhead, and large data transfers during learning. To solve this issue, the paper proposes SplitFedZip, which uses learned compression to reduce data transfer in SplitFed learning. The results show that learned compression can significantly reduce data transmission while maintaining model accuracy. |
Keywords
» Artificial intelligence » Federated learning » Image segmentation
