Model Sparsification for Communication-Efficient Multi-Party Learning via Contrastive Distillation in Image Classification

Feng, Kaiyuan; Gong, Maoguo; Pan, Ke; Zhao, Hongyu; Wu, Yue; Sheng, Kai

doi:10.1109/tetci.2023.3268713

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2024

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Cited by 3 publications

References 35 publications

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Model Optimization Techniques for Edge Devices

Nimmagadda

2024

Model Optimization Methods for Efficient and Edge AI

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Model Optimization Techniques for Edge Devices

Nimmagadda

2024

Model Optimization Methods for Efficient and Edge AI

View full text Add to dashboard Cite

rFedFW: Secure and trustable aggregation scheme for Byzantine-robust federated learning in Internet of Things

Ni,

Gong,

et al. 2024

Information Sciences

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Traffic Sign Recognition Using Optimized Federated Learning in Internet of Vehicles

Lian,

Zeng,

Wang

et al. 2024

IEEE Internet Things J.

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Traffic Sign Recognition (TSR) is vital for vehicle safety and navigation, especially in the era of autonomous cars. Internet of Vehicles (IoV) provide a promising infrastructure for vehicular networks due to their agility and interoperability. However, privacy concerns and network restrictions hinder the collection of massive data from distributed automotive sensors in IoV. To address these challenges, this paper proposes the application of Federated Learning (FL) and model sparsification to optimize TSR in autonomous vehicles. FL enables decentralized learning while preserving data privacy, and model sparsification significantly reduces communication costs. Furthermore, we incorporate the Adam optimizer for local training, ensuring efficient model optimization on each vehicle. Experimental results demonstrate the effectiveness of our approach, with improved TSR performance while mitigating privacy risks and enhancing communication efficiency. This research contributes to the advancement of TSR in IoV by introducing FL, model sparsification, and the use of the Adam optimizer for local training, facilitating efficient and privacy-preserving vehicular network learning.

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Model Sparsification for Communication-Efficient Multi-Party Learning via Contrastive Distillation in Image Classification

Cited by 3 publications

References 35 publications

Model Optimization Techniques for Edge Devices

Model Optimization Techniques for Edge Devices

rFedFW: Secure and trustable aggregation scheme for Byzantine-robust federated learning in Internet of Things

Traffic Sign Recognition Using Optimized Federated Learning in Internet of Vehicles

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