Summary of Korea-sfl: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting, by Zeke Xia and Ming Hu and Dengke Yan and Ruixuan Liu and Anran Li and Xiaofei Xie and Mingsong Chen
KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting
by Zeke Xia, Ming Hu, Dengke Yan, Ruixuan Liu, Anran Li, Xiaofei Xie, Mingsong Chen
First submitted to arxiv on: 19 Apr 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 novel Split Federated Learning approach, called KoReA-SFL, addresses the issues of low training accuracy and catastrophic forgetting in traditional SFL methods by adopting a multi-model aggregation mechanism and knowledge replay strategy. This approach maintains multiple branch model portions on cloud servers for local training and aggregated master-model portion for knowledge sharing among branch portions. The main server selects assistant devices for knowledge replay based on data distribution, allowing for improved test accuracy up to 23.25%. This method demonstrates significant performance improvement in both non-IID and IID scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KoReA-SFL is a new way of doing Split Federated Learning that helps with sharing knowledge among devices. Right now, this type of learning has a problem where the training accuracy is low because it doesn’t take into account differences in data between devices. To solve this issue, KoReA-SFL uses multiple models and replaying old knowledge to help the devices learn better. It even chooses which devices get to share their knowledge based on how much they have learned. This new approach works really well, increasing test accuracy by up to 23.25%. |
Keywords
» Artificial intelligence » Federated learning
