Efficient Cross-Domain Federated Learning by MixStyle Approximation

no code implementations • 12 Dec 2023 • Manuel Röder, Leon Heller, Maximilian Münch, Frank-Michael Schleif

With the advent of interconnected and sensor-equipped edge devices, Federated Learning (FL) has gained significant attention, enabling decentralized learning while maintaining data privacy.

Federated Learning Privacy Preserving

Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective

1 code implementation • 18 Dec 2021 • Simon Heilig, Maximilian Münch, Frank-Michael Schleif

Matrix approximations are a key element in large-scale algebraic machine learning approaches.

Complex-valued embeddings of generic proximity data

no code implementations • 31 Aug 2020 • Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif

As an information preserving alternative, we propose a complex-valued vector embedding of proximity data.

BIG-bench Machine Learning Generalization Bounds