Literature Database Entry


Mengfan Wu, Mate Boban and Falko Dressler, "Flexible Training and Uploading Strategy for Asynchronous Federated Learning in Dynamic Environments," IEEE Transactions on Mobile Computing, June 2024. (online first)


Federated learning is a fast-developing distributed learning scheme with promising applications in vertical domains such as industrial automation and connected automated driving. The heterogeneity of devices in data distribution, communication, and computation, when deployed in dynamic environments typically with wireless communication, poses challenges to traditional federated learning solutions, where successful learning depends on balanced contribution from participants. In this paper, we propose a flexible communication strategy for devices in asynchronous federated learning, which adapts the training and uploading actions based on the condition of the communication link. We propose a novel method of computing aggregation weight based on model distances and number of local optimizations, to control errors introduced in asynchronous aggregation while maximizing learning speed. We prove the convergence of the learning tasks analytically under the new scheme. The improved performance is rooted in the increased number of optimizations during training, which grows by 12% through opportunistically condensing model uploading during good link condition periods. By facilitating timely communication between devices and server, combined with the novel aggregation weight design, our method reduces the communication resources in dynamic environments by at least 5% while even slightly increasing the learning accuracy.

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/TMC.2024.3418613},
    note = {to appear},
    title = {{Flexible Training and Uploading Strategy for Asynchronous Federated Learning in Dynamic Environments}},
    journal = {IEEE Transactions on Mobile Computing},
    issn = {1536-1233},
    publisher = {IEEE},
    month = {6},
    year = {2024},

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.

Last modified: 2024-07-21