Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks

Yan, Zhigang; Li, Dong

doi:10.1109/tcomm.2024.3362143

IEEE Trans. Commun.

2024

DOI: 10.1109/tcomm.2024.3362143

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Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks

Zhigang Yan,

Dong Li

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2024

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Cited by 3 publications

References 45 publications

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Decentralized Federated Learning on the Edge: From the Perspective of Quantization and Graphical Topology

Yan,

2024

IEEE Internet Things J.

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Decentralized Federated Learning on the Edge: From the Perspective of Quantization and Graphical Topology

Yan,

2024

IEEE Internet Things J.

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Topology Learning for Heterogeneous Decentralized Federated Learning Over Unreliable D2D Networks

Wu,

Xu,

Zeng

et al. 2024

IEEE Trans. Veh. Technol.

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Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications

Naseh,

Abdollahpour,

Tarchi

2024

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This paper explores the practical implementation and performance analysis of distributed learning (DL) frameworks on various client platforms, responding to the dynamic landscape of 6G technology and the pressing need for a fully connected distributed intelligence network for Internet of Things (IoT) devices. The heterogeneous nature of clients and data presents challenges for effective federated learning (FL) techniques, prompting our exploration of federated transfer learning (FTL) on Raspberry Pi, Odroid, and virtual machine platforms. Our study provides a detailed examination of the design, implementation, and evaluation of the FTL framework, specifically adapted to the unique constraints of various IoT platforms. By measuring the accuracy of FTL across diverse clients, we reveal its superior performance over traditional FL, particularly in terms of faster training and higher accuracy, due to the use of transfer learning (TL). Real-world measurements further demonstrate improved resource efficiency with lower average load, memory usage, temperature, power, and energy consumption when FTL is implemented compared to FL. Our experiments also showcase FTL’s robustness in scenarios where users leave the server’s communication coverage, resulting in fewer clients and less data for training. This adaptability underscores the effectiveness of FTL in environments with limited data, clients, and resources, contributing valuable information to the intersection of edge computing and DL for the 6G IoT.

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Performance Analysis for Resource Constrained Decentralized Federated Learning Over Wireless Networks

Cited by 3 publications

References 45 publications

Decentralized Federated Learning on the Edge: From the Perspective of Quantization and Graphical Topology

Decentralized Federated Learning on the Edge: From the Perspective of Quantization and Graphical Topology

Topology Learning for Heterogeneous Decentralized Federated Learning Over Unreliable D2D Networks

Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications

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