Fed-mRMR: A lossless federated feature selection method
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Fed-mRMR: A lossless federated feature selection methodDate
2024-05Citation
Jorge Hermo, Verónica Bolón-Canedo, and Susana Ladra, "Fed-mRMR: A lossless federated feature selection method", Information Sciences, Vol. 669, 120609, May 2024, doi: 10.1016/j.ins.2024.120609
Abstract
[Abstract]: Feature selection has become a mandatory task in data mining, due to the overwhelming amount of features in Big Data problems. To handle this high-dimensional data and avoid the well-known curse of dimensionality, we need to pre-select an optimal subset of features to reduce redundant computations. Federated learning is a machine learning technique based on training an algorithm over many decentralized edge devices holding local rather than global data on a centralized server. Application of this technique is extending to fields such as self-driving cars, medicine and health, and Industry 4.0, where data privacy is compulsory. Feature selection through federated learning is a complicated task since suboptimal features calculated by feature selection methods may be different in heterogeneous datasets from different nodes. In this paper, we propose a lossless federated version of the classic minimum redundancy maximum relevance (mRMR) feature selection algorithm, called federated mRMR (fed-mRMR), which, without losing any effectiveness of the original mRMR method, is applicable to federated learning approaches and capable of dealing with data that are not independent and identically distributed (non-IID data). Implementation can be found at: https://github.com/jorgehermo9/fed-mrmr
Keywords
Edge computing
Feature selection
Federated learning
Machine learning
Non-IID data
Privacy preservation
Feature selection
Federated learning
Machine learning
Non-IID data
Privacy preservation
Description
Implementation can be found at: https://github.com/jorgehermo9/fed-mrmr
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Atribución 3.0 España