Literature Database Entry

zhang2025fed-raa

Ruirui Zhang, Xingze Wu, Yifei Zou, Zhenzhen Xie, Peng Li, Xiuzhen Cheng, Falko Dressler and Dongxiao Yu, "Fed-RAA: Resource-Adaptive Asynchronous Federated Edge Learning with Theoretical Guarantee," IEEE Transactions on Mobile Computing, December 2025. (online first)

Abstract

This paper studies an efficient federated learning (FL) problem involving multiple edge-based clients with heterogeneous constrained resources. Compared with numerous training parameters, the computing and communication resources of clients in edge scenarios are usually insufficient for fast local training and real-time knowledge sharing. Besides, training on clients with heterogeneous resources may result in the straggler problem, which delays the convergence of FL. To address these issues, we propose Fed-RAA: a Resource-Adaptive Asynchronous Federated learning algorithm. Different from vanilla FL methods, where all parameters are trained by each participating client regardless of resource diversity, Fed-RAA adaptively allocates submodels of the global model to clients based on their computing and communication capabilities. Each client then individually trains its assigned submodel and asynchronously uploads the updated result. Theoretical analysis confirms the convergence of our approach. Additionally, an online greedy-based algorithm is designed for asynchronous submodel assignment in Fed-RAA, improving the convergence of Fed-RAA by optimal minimization on the training delay bound of submodels. Compared to state-of-the-art methods, our Fed-RAA algorithm reduces the time required to achieve the target accuracy by an average of 30.89%, demonstrating its superior efficiency on heterogeneous constrained computing and communication resources. To the best of our knowledge, this paper is the first resource-adaptive asynchronous method for submodel-based FL with guaranteed theoretical convergence.

Quick access

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

Contact

Ruirui Zhang
Xingze Wu
Yifei Zou
Zhenzhen Xie
Peng Li
Xiuzhen Cheng
Falko Dressler
Dongxiao Yu

BibTeX reference

@article{zhang2025fed-raa,
    author = {Zhang, Ruirui and Wu, Xingze and Zou, Yifei and Xie, Zhenzhen and Li, Peng and Cheng, Xiuzhen and Dressler, Falko and Yu, Dongxiao},
    doi = {10.1109/TMC.2025.3649551},
    note = {to appear},
    title = {{Fed-RAA: Resource-Adaptive Asynchronous Federated Edge Learning with Theoretical Guarantee}},
    journal = {IEEE Transactions on Mobile Computing},
    issn = {1536-1233},
    publisher = {IEEE},
    month = {12},
    year = {2025},
   }
   
   

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 permissions@acm.org.

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

This page was automatically generated using BibDB and bib2web.

Last modified: 2026-01-23