A Privacy-Preserving Federated Learning Approach for Kernel methods
This addresses privacy concerns in federated learning for kernel methods, particularly in sensitive domains like healthcare, though it appears incremental as it builds on existing federated and kernel method frameworks.
The paper tackles the challenge of implementing kernel methods on distributed data with strict privacy requirements, such as clinical data, by proposing FLAKE, a federated learning approach that enables exact privacy-preserving computation of kernel matrices without noise, and experiments show it outperforms comparable methods in accuracy and efficiency.
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.