Federated learning: a comparison of methods

Federated learning: a comparison of methods: How do different Federated Learning frameworks compare?

Cristea, Vlad (TU Delft Electrical Engineering, Mathematics and Computer Science)

Garst, S.J.F. (mentor)
Reinders, M.J.T. (graduation committee)
Chen, Lydia Y. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-28

Federated Learning is a machine learning paradigm for decentralized training over different clients. The training happens in rounds where each client learns a specific model which is then aggregated by a central server and passed back to the clients. Since the paradigm’s inception, many frameworks that provide Federated Learning tools and infrastructure have appeared. This leads to the question of ”How do different Federated Learning frameworks compare?”, which is the research question of this paper. The paper’s main contribution will be helping developers new to the Federated Learning field decide between NVidia Flare, OpenFL, and Flower, three popular federated learning frameworks.

Federated Learning
Benchmarking
Nvidia Flare
OpenFL
Flower

http://resolver.tudelft.nl/uuid:e82d6205-07c7-4872-bdab-4226baa2830a

Student theses

bachelor thesis

© 2023 Vlad Cristea