Summary of Federated Learning For Non-factorizable Models Using Deep Generative Prior Approximations, by Conor Hassan et al.
Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations
by Conor Hassan, Joshua J Bon, Elizaveta Semenova, Antonietta Mira, Kerrie Mengersen
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO); Methodology (stat.ME)
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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 Structured Independence via deep Generative Model Approximation (SIGMA) prior allows for federated learning (FL) of non-factorizable models across decentralized clients, expanding the applicability of FL to fields like spatial statistics, epidemiology, environmental science, and more. The SIGMA prior is a pre-trained deep generative model that approximates the desired prior and induces a specified conditional independence structure in latent variables, creating an approximate model suitable for FL settings. By leveraging Gaussian processes (GPs) as priors, this approach enables accurate modeling of dependencies between clients’ models. Experimental results on synthetic data demonstrate the effectiveness of SIGMA, with a real-world example showcasing its utility in spatial data analysis across Australia. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for different computers or devices to work together and learn from each other without sharing their own personal information. The problem is that most current methods assume that the information they’re working with doesn’t depend on anything else, which isn’t always true. For example, in environmental science, the temperature at one location might be affected by the temperature at another location nearby. This paper introduces a new way of thinking about this called SIGMA, which allows different devices to work together even when their information is dependent. It does this by using something called Gaussian processes (GPs) that can model these dependencies. The authors tested this approach and found it worked well on some data they made up, as well as in a real-world example of analyzing temperature patterns across Australia. |
Keywords
» Artificial intelligence » Federated learning » Generative model » Synthetic data » Temperature
