Summary of Analytic Federated Learning, by Huiping Zhuang et al.
Analytic Federated Learning
by Huiping Zhuang, Run He, Kai Tong, Di Fang, Han Sun, Haoran Li, Tianyi Chen, Ziqian Zeng
First submitted to arxiv on: 25 May 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 In this paper, the authors introduce analytic federated learning (AFL), a new training paradigm that combines analytical solutions with federated learning. AFL draws inspiration from analytic learning, which trains neural networks in one epoch using closed-form solutions. The authors show that AFL eliminates the need for multi-epoch updates during local client training and derives an absolute aggregation law for single-round aggregations. They also demonstrate that AFL exhibits a weight-invariant property, making it suitable for scenarios with data heterogeneity and large numbers of clients. Experimental results across various FL settings, including non-IID datasets and 1000+ clients, show that AFL performs competitively while existing techniques encounter obstacles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to learn together in a distributed setting called federated learning (FL). The authors use a special method called analytic learning that can train neural networks quickly. They combine this with FL and call it analytic federated learning (AFL). AFL is good because it makes the results the same even if the data is different on each device, or if there are many devices. This could be useful in real-life situations where data is not always the same. The authors tested AFL on some examples and found that it works well. |
Keywords
» Artificial intelligence » Federated learning
