Summary of Communication-efficient Distributed Learning with Local Immediate Error Compensation, by Yifei Cheng et al.
Communication-Efficient Distributed Learning with Local Immediate Error Compensation
by Yifei Cheng, Li Shen, Linli Xu, Xun Qian, Shiwei Wu, Yiming Zhou, Tie Zhang, Dacheng Tao, Enhong Chen
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Local Immediate Error Compensated SGD (LIEC-SGD) optimization algorithm aims to reduce communication overhead in distributed learning by combining bidirectional compression and carefully designed compensation approaches. This method outperforms previous works in both convergence rate and communication cost, inheriting the advantages of unidirectional and bidirectional compression. LIEC-SGD trains deep neural networks effectively, making it a promising solution for distributed learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LIEC-SGD is an algorithm that helps computers learn together without using too much internet bandwidth. It does this by compressing information twice (in two directions) to reduce the amount of data sent between devices. This allows computers to learn faster and use less internet, which can be important for tasks like training artificial intelligence models. The algorithm was tested on deep neural networks and shown to work well. |
Keywords
* Artificial intelligence * Optimization
