FairMoE-FL: A Communication-Efficient and Fair Federated Mixture-of-Experts Framework
Document Type
Conference Proceeding
Source of Publication
IEEE International Conference on Communications
Publication Date
5-24-2026
Abstract
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data but suffers from communication overhead and unfair performance due to client heterogeneity. We propose FairMoE-FL, a novel FL framework that integrates a Mixture-of-Experts (MoE) architecture with fairness-aware optimization. FairMoE-FL employs sparse expert activation and dual-level routing with lightweight local adapters, reducing communication while preserving personalization. To ensure equitable outcomes, we introduce fairness-regularized objectives that penalize accuracy disparity, balance expert usage, and incorporate communication cost constraints. Extensive experiments on MNIST and CIFAR-10 show that FairMoE-FL consistently improves accuracy and fairness across clients while transmitting fewer parameters than baseline methods. These results highlight the potential of conditional computation with fairness-aware optimization for scalable, efficient, and equitable FL in heterogeneous environments.
DOI Link
ISBN
[9798319542090]
ISSN
Publisher
IEEE
Disciplines
Computer Sciences
Keywords
Communication Efficiency, Fairness, Federated Learning, Mixture-of-Experts
Scopus ID
Recommended Citation
Tariq, Asadullah; Serhani, Mohamed Adel; Abdelmoniem, Ahmed M.; Din, Irfanud; and Taleb, Ikbal, "FairMoE-FL: A Communication-Efficient and Fair Federated Mixture-of-Experts Framework" (2026). All Works. 8186.
https://zuscholars.zu.ac.ae/works/8186
Indexed in Scopus
yes
Open Access
no