Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by...
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Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML frameworks require resource-constrained devices to update the entire Deep Neural Network (DNN) models and are vulnerable to reconstruction attacks. Concurrently, the serial DCML frameworks suffer from training efficiency problems due to their serial training nature. In this paper, we propose a Model Pruning-enabled Federated Split Learning framework (MP-FSL) to reduce resource consumption with a secure and efficient training scheme. Specifically, MP-FSL compresses DNN models by adaptive channel pruning and splits each compressed model into two parts that are assigned to the client and the server. Meanwhile, MP-FSL adopts a novel aggregation algorithm to aggregate the pruned heterogeneous models. We implement MP-FSL with a real FL platform to evaluate its performance. The experimental results show that MP-FSL outperforms the state-of-the-art frameworks in model accuracy by up to 1.35%, while concurrently reducing storage and computational resource consumption by up to 32.2% and 26.73%, respectively. These results demonstrate that MP-FSL is a comprehensive solution to the challenges faced by DCML, with superior performance in both reduced resource consumption and enhanced model performance.
After a thorough peer-review process, the 17th SOCO 2022 International Program Committee selected 64 papers which are published in these conference proceedings and represent an acceptance rate of 60%. In this relevant...
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ISBN:
(数字)9783031180507
ISBN:
(纸本)9783031180491
After a thorough peer-review process, the 17th SOCO 2022 International Program Committee selected 64 papers which are published in these conference proceedings and represent an acceptance rate of 60%. In this relevant edition, a particular emphasis was put on the organization of special sessions. Seven special sessions were organized related to relevant topics such as machine learning and computer vision in Industry 4.0; time series forecasting in industrial and environmental applications; optimization, modeling, and control by soft computing techniques; soft computing applied to renewable energy systems; preprocessing big data in machine learning; tackling real-world problems with artificial intelligence.
This book presents the best-selected research papers presented at the Third International Conference on computing, Communication, Security & Intelligent Systems (IC3SIS 2024), organized by SCMS School of Engineeri...
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ISBN:
(数字)9789819602285
ISBN:
(纸本)9789819602278;9789819602308
This book presents the best-selected research papers presented at the Third International Conference on computing, Communication, Security & Intelligent Systems (IC3SIS 2024), organized by SCMS School of Engineering and Technology, Kochi, on July 11–12, 2024. It discusses the latest technologies in communication and intelligent systems, covering various areas of computing, such as advanced computing, communication and networking, intelligent systems and analytics, 5G and IoT, soft computing, and cybersecurity in general. Featuring work by leading researchers and technocrats, the book serves as a valuable reference resource for young researchers, academics, and industry practitioners.
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