Self-supervised Federated learning at the edge
F.M. Heijink (TU Delft - Electrical Engineering, Mathematics and Computer Science)
F.A. van Pelt (TU Delft - Applied Sciences)
Charlotte Frenkel – Mentor (TU Delft - Electronic Instrumentation)
Justin Dauwels – Mentor (TU Delft - Signal Processing Systems)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
This report serves to finalize the bachelor graduation project on the topic of self-supervised federated learning, specifically the implementation of the algorithms in Python. The goal of the project is to implement a self-supervised learning setup in a decentralized approach using Field-Programmable Gate Arrays (FPGAs) for the processing of data. This serves as a proof of concept that decentralized machine learning on unlabeled data using FPGAs is possible. Multiple algorithms based on the literature were considered to allow for a low-profile learning setup, with simplifications done to be able to reduce the compute required. The results are promising: scaled-down models that can run on an FPGA show that self-supervised learning functions as expected from the theory. By decentralizing the computations increases in performance are possible in favorable conditions. The authors hope that the concept of self-supervised federated learning can be employed to FPGAs on a larger scale to help in the processing of the abundant yet underutilized unlabeled data present at the edges of information networks.