Asynchronous Federated Split Learning
Journal article, 2024

We propose a first Asynchronous Federated Split Learning (AFSL), to add the flexibility of asynchronous computing to the combination of federated and split learning. This amounts to designing a harmonious combination of different paradigms in order to benefit from the advantages of each of them and to reduce the impacts of their shortcomings.This way, AFSL answers to the increasingly rising interest for distributed algorithms with the advent of edge computing in order to support new market segments, such as cloud gaming, immersive eXtended Reality (XR), indoor positioning, and mission critical IoT networks, with stringent requirements on latency and reliability.Computational experiments are conducted on IID and non-IID datasets to investigate the added value of the asynchronous feature. Results indicate that AFSL can accelerate model learning by up to 86% without sacrificing the model's convergence and accuracy. Indeed, not only average training times are reduced, but clients use fewer resources, a critical characteristic for devices with limited computing capabilities, e.g., in edge devices. Performance degradation can be mitigated by a careful selection of the aggregation principle. Other advantages are with AFSL training in dynamic scenarios as it provides robustness with a short recovery time by leveraging asynchronous client training.

Asynchronous

Distributed machine learning

Edge computing

Federated learning

Split learning

Author

R. A. Albuquerque

Concordia University

L. P. Dias

Concordia University

Momo Ziazet

Concordia University

K. Vandikas

Ericsson

S. Ickin

Ericsson

B. Jaumard

Ericsson

Carlos Natalino Da Silva

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Lena Wosinska

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Paolo Monti

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Elaine Wong

University of Melbourne

Proceedings of the IEEE International Conference on Fog and Edge Computing, ICFEC

26943263 (ISSN) 26943255 (eISSN)

2024 11-18

Subject Categories

Computer Science

Computer Systems

DOI

10.1109/ICFEC61590.2024.00010

More information

Latest update

11/22/2024