Summary of Non-federated Multi-task Split Learning For Heterogeneous Sources, by Yilin Zheng et al.
Non-Federated Multi-Task Split Learning for Heterogeneous Sources
by Yilin Zheng, Atilla Eryilmaz
First submitted to arxiv on: 31 May 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 In this paper, researchers address the challenge of serving heterogeneous data sources at the network edge by designing a new distributed machine learning mechanism. They propose an alternative paradigm called Multi-Task Split Learning (MTSL) that combines Split Learning (SL) with distributed network architectures. MTSL leverages heterogeneity as a useful property rather than an obstacle to overcome. The authors demonstrate the effectiveness of MTSL through theoretical analysis and numerical experiments on image classification datasets, showing faster convergence, lower communication costs, and improved robustness compared to existing multi-task Federated Learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to do machine learning when you have lots of different kinds of data. Right now, we use something called Federated Learning (FL) to make sure our models are good at many things. But FL has some problems when the data is very different. So, the researchers in this paper came up with a new idea called Multi-Task Split Learning (MTSL). MTSL lets the model learn from all the different kinds of data and use that to its advantage. The authors tested their idea on some image classification tasks and showed that it’s better than FL in many ways. |
Keywords
» Artificial intelligence » Federated learning » Image classification » Machine learning » Multi task
