Performance Evaluation of Decentralized Federated Learning: Impact of Fully and K-Connected Topologies, Heterogeneous Computing Resources, and Communication Bandwidth

Performance Evaluation of Decentralized Federated Learning: Impact of Fully and K-Connected Topologies, Heterogeneous Computing Resources, and Communication Bandwidth

Decentralized federated learning (DFL) enables collaborative model training across distributed devices while preserving data privacy. Despite this, the performance of DFL in real-world scenarios, characterized by varying network topologies, heterogeneous client resources, and communication bandwidth...

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Bibliographic Details
Main Authors: Vo van Truong, Pham Khanh Quan, Dong-Hwan Park, Taehong Kim
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Federated learning
decentralized learning
network topology
heterogeneity
communication bandwidth
Online Access:https://ieeexplore.ieee.org/document/10891509/
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https://ieeexplore.ieee.org/document/10891509/

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