Summary of A Federated Distributionally Robust Support Vector Machine with Mixture Of Wasserstein Balls Ambiguity Set For Distributed Fault Diagnosis, by Michael Ibrahim et al.
A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis
by Michael Ibrahim, Heraldo Rozas, Nagi Gebraeel, Weijun Xie
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 paper tackles a crucial task in original equipment manufacturers’ (OEMs) long-term service contracts (LTSCs): training classification models for fault diagnosis tasks using geographically dispersed data. Due to privacy and bandwidth constraints, the model must be trained in a federated manner. The authors propose a distributionally robust (DR) support vector machine (SVM) trained in a federated fashion over a network of clients without sharing data. They introduce the Mixture of Wasserstein Balls (MoWB) ambiguity set and study its theoretical aspects, demonstrating out-of-sample performance guarantees and separability of the DR problem. Two distributed optimization algorithms are proposed: subgradient method-based and alternating direction method of multipliers (ADMM)-based. The authors provide closed-form expressions for central server computations during each iteration. Numerical experiments utilizing simulation data and real-world datasets demonstrate the algorithms’ performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about training a special kind of computer model to help companies predict when their products might break down. This is important because it helps them offer better services to customers who buy those products. The problem is that the data used to train this model comes from many different places, and it’s hard to combine all that information without sharing sensitive details. The authors suggest a new way to do this using something called “distributionally robust” support vector machines. They also propose two methods for training these models: one uses an iterative process, and the other uses a special algorithm. Finally, they test their ideas on real-world data to show how well they work. |
Keywords
» Artificial intelligence » Classification » Optimization » Support vector machine
