Secure Federated Learning with Kernel Affine Hull Machines
Mohit Kumar, Bernhard Moser, Lukas Fischer · 2023
The concept of Kernel Affine Hull Machine (KAHM) was recently introduced for representing data via learning in Reproducing Kernel Hilbert Spaces.KAHM defines a bounded geometric body in data space such that a distance measure from the geometric body can be used to aggregate local KAHM-based models to build a global model.This study leverages KAHMs for secure federated learning where data is protected from an aggressive aggregator by fully homomorphic encryption.An accurate and computationally efficient federated learning architecture, that combines local KAHMs-based classifiers in a robust and flexible manner such that the global model can be homomorphically evaluated in an efficient manner, is provided. * The research reported in this paper has been supported by the Austrian Research Promotion Agency (FFG) COMET-Modul S3AI; FFG Grant SMiLe; BMK, BMAW, and the State of Upper