Cluster-Based Device Scheduling Design for Semi-Asynchronous Federated Learning in Mobile Edge Computing Networks

In mobile edge computing (MEC) networks, federated learning (FL) has emerged as the leading distributed framework for training a shared machine learning model, primarily benefiting from its ability to exchange the information of edge devices (EDs) while safeguarding their privacy. However, in MEC ne...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE International Conference on Communications (2003) S. 1590 - 1595
Hauptverfasser: Zeng, Hushuang, Li, Xiuhua, Xu, Guozeng, Hao, Jinlong, Wang, Xiaofei, Leung, Victor C. M.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 08.06.2025
Schlagworte:
Clustering algorithms
Computational modeling
Convergence
Federated learning
Multi-access edge computing
Performance evaluation
Privacy
Processor scheduling
Training
ISSN:1938-1883
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In mobile edge computing (MEC) networks, federated learning (FL) has emerged as the leading distributed framework for training a shared machine learning model, primarily benefiting from its ability to exchange the information of edge devices (EDs) while safeguarding their privacy. However, in MEC networks, the heterogeneity of communication, computation, and data can result in challenges such as stragglers and data imbalances, thereby impeding the training process of FL. To address these challenges, we propose a Semi-Asynchronous Federated Learning (Semi-AFL) framework with cluster-based scheduling. In Semi-AFL, the EDs can perform local training at their own pace using different stale global models to tackle the straggler effect. Considering the asynchronousity of Semi-AFL and data heterogeneity, we propose a cluster-based scheduling strategy that includes device clustering and device selection. Specifically, it performs clustering based on the label distribution and obtains device-to-cluster information. We further select devices based on clustering information as well as model staleness and contribution, aiming to reduce variance and bias and accelerate model convergence. Experiment results demonstrate the effectiveness of the proposed method in reducing the latency of FL.
ISSN:1938-1883
DOI:10.1109/ICC52391.2025.11161427