Summary of Federated Scientific Machine Learning For Approximating Functions and Solving Differential Equations with Data Heterogeneity, by Handi Zhang et al.
Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity
by Handi Zhang, Langchen Liu, Lu Lu
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Physics (physics.comp-ph)
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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 emerging field of scientific machine learning (SciML) leverages neural networks to address complex problems governed by partial differential equations (PDEs). This paper integrates Federated Learning (FL) with SciML to approximate complex functions and solve PDEs. The authors propose two novel models, federated physics-informed neural networks (FedPINN) and federated deep operator networks (FedDeepONet), which enable collaborative training while preserving data privacy. They also introduce various data generation methods to control non-independent and identically distributed (non-iid) data and quantify data heterogeneity using the 1-Wasserstein distance. The paper systematically investigates the relationship between data heterogeneity and federated model performance, proposing a measure of weight divergence and establishing growth bounds for weight divergence in federated learning compared to traditional centralized learning. Experiments demonstrate that proposed federated methods surpass models trained only using local data and achieve competitive accuracy with centralized models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines artificial intelligence and machine learning to solve complex math problems. It uses a new way of training models called Federated Learning, which helps keep data private by not sharing it. The authors created two special kinds of neural networks that can work together to solve problems even when the data is spread out or different. They also came up with ways to make sure the data is all similar and compared how well their new methods worked against traditional ways. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
