A Privacy-Preserving Federated Learning Approach for Kernel methods

TL;DR

This paper introduces FLAKE, a federated learning method enabling privacy-preserving kernel computations on distributed data without noise, suitable for sensitive applications like clinical data, and demonstrating high efficiency and accuracy.

Contribution

FLAKE is a novel federated approach that allows exact privacy-preserving kernel computations on distributed data without adding noise, improving efficiency and accuracy.

Findings

FLAKE prevents data leakage under semi-honest threat model.

Experiments show FLAKE outperforms comparable methods in accuracy.

Data masking and Gram matrix computation are significantly faster than SVM training.

Abstract

It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, if the use case rules out privacy-preserving approaches that introduce noise. An example for such a use case is machine learning on clinical data. To realize exact privacy preserving computation of kernel methods, we propose FLAKE, a Federated Learning Approach for KErnel methods on horizontally distributed data. With FLAKE, the data sources mask their data so that a centralized instance can compute a Gram matrix without compromising privacy. The Gram matrix allows to calculate many kernel matrices, which can be used to train kernel-based machine learning algorithms such as Support Vector Machines. We prove that FLAKE prevents an adversary from learning the input data or the number of input features under a semi-honest threat model. Experiments on clinical and synthetic data confirm that FLAKE is outperforming the accuracy and efficiency of comparable methods. The time needed to mask the data and to compute the Gram matrix is several orders of magnitude less than the time a Support Vector Machine needs to be trained. Thus, FLAKE can be applied to many use cases.