Literature Database Entry


Mengfan Wu, Mate Boban and Falko Dressler, "Parameter-less Asynchronous Federated Learning under Computation and Communication Constraints," Proceedings of 97th IEEE Vehicular Technology Conference (VTC 2023-Spring), Florence, Italy, June 2023, pp. 1–7.


Federated Learning is a fast-developing distributed learning scheme that has promising applications in vertical domains such as industrial automation and connected automated driving. In this paper we address the heterogeneity of the participation of devices in federated learning caused by: i) non-uniform distribution of local data; ii) uneven and varying computational resources across the devices; and iii) dynamic communication link. We propose a quasi-dynamic simulation scheme allowing realistic approximation of these three factors of heterogeneity. Aggregation schemes at the server based on the clients’ work status are implemented. We show that the new asynchronous aggregation algorithm does not require tuning of hyper-parameters such as the round time in synchronous federated learning and the aggregation weight in classic asynchronous aggregation, while providing better or comparable performance in terms of accuracy and convergence speed.

Quick access

Original Version DOI (at publishers web site)
Authors' Version PDF (PDF on this web site)
BibTeX BibTeX


Mengfan Wu
Mate Boban
Falko Dressler

BibTeX reference

    author = {Wu, Mengfan and Boban, Mate and Dressler, Falko},
    doi = {10.1109/VTC2023-Spring57618.2023.10200520},
    title = {{Parameter-less Asynchronous Federated Learning under Computation and Communication Constraints}},
    pages = {1--7},
    publisher = {IEEE},
    issn = {2577-2465},
    address = {Florence, Italy},
    booktitle = {97th IEEE Vehicular Technology Conference (VTC 2023-Spring)},
    month = {6},
    year = {2023},

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at

This page was automatically generated using BibDB and bib2web.