# A Privacy-Preserving Federated Learning Approach for Kernel methods

arXiv: https://arxiv.org/abs/2306.02677 · HTML: https://mlsanity.com/paper/2306.02677/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [One-Shot Federated Learning](https://mlsanity.com/paper/1902.11175/) (2019)
- [Dopamine: Differentially Private Federated Learning on Medical Data](https://mlsanity.com/paper/2101.11693/) (2021)
- [Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption](https://mlsanity.com/paper/2108.03437/) (2021)
- [Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case](https://mlsanity.com/paper/2204.07752/) (2022)
- [ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare](https://mlsanity.com/paper/2012.02688/) (2020)

## Built on by (most cited first, 5 of 5)

- [Federated Learning in Genetics: Extended Analysis of Accuracy, Performance and Privacy Trade-offs](https://mlsanity.com/paper/2402.14527/) (2024)
- [Distributed and Secure Kernel-Based Quantum Machine Learning](https://mlsanity.com/paper/2408.10265/) (2024)
- [Private, Efficient and Scalable Kernel Learning for Medical Image Analysis](https://mlsanity.com/paper/2410.15840/) (2024)
- [Privacy-Preserving Federated Unsupervised Domain Adaptation for Regression on Small-Scale and High-Dimensional Biological Data](https://mlsanity.com/paper/2411.17287/) (2024)
- [Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning](https://mlsanity.com/paper/2509.10635/) (2025)

## Abstract (arXiv)

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.