Summary of Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization, by Yunrui Sun and Gang Hu and Yinglei Teng and Dunbo Cai
Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization
by Yunrui Sun, Gang Hu, Yinglei Teng, Dunbo Cai
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Neural and Evolutionary Computing (cs.NE)
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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 Heterogeneous Split Federated Learning (HSFL) framework enables resource-constrained devices to train personalized models simultaneously, leveraging different cut layers. This approach tackles limitations in training efficiency and prolonged latency in sequential settings by optimizing computational and transmission resources jointly. HSFL outperforms other frameworks in terms of convergence rate and model accuracy on heterogeneous devices with non-iid data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers train models without sharing all their information is called Split Learning (SL). But, current SL methods can take a long time and use too much energy. To fix this, researchers created the Heterogeneous Split Federated Learning (HSFL) framework. It lets devices train their own models at the same time, using different parts of the model. This makes it faster and more efficient. |
Keywords
* Artificial intelligence * Federated learning
