A Hybrid Federated Kernel Regularized Least Squares Algorithm

TL;DR

This paper introduces a novel hybrid federated kernel regularized least squares algorithm designed for privacy-preserving machine learning across distributed clinical and omics data, with validation on standard datasets and security considerations.

Contribution

It proposes a reformulated federated kernel method with two variants tailored for hybrid data distribution scenarios, advancing privacy-preserving machine learning techniques.

Findings

Validated on well-established datasets

Enhanced privacy with security measures

Effective in hybrid data distribution scenarios

Abstract

Federated learning is becoming an increasingly viable and accepted strategy for building machine learning models in critical privacy-preserving scenarios such as clinical settings. Often, the data involved is not limited to clinical data but also includes additional omics features (e.g. proteomics). Consequently, data is distributed not only across hospitals but also across omics centers, which are labs capable of generating such additional features from biosamples. This scenario leads to a hybrid setting where data is scattered both in terms of samples and features. In this hybrid setting, we present an efficient reformulation of the Kernel Regularized Least Squares algorithm, introduce two variants and validate them using well-established datasets. Lastly, we discuss security measures to defend against possible attacks.