Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge Computing Environment

Author:

Jia Yongzhe1ORCID,Liu Bowen1ORCID,Zhang Xuyun2ORCID,Dai Fei3ORCID,Khan Arif4ORCID,Qi Lianyong5ORCID,Dou Wanchun1ORCID

Affiliation:

1. State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, China

2. Department of Computing, Macquarie University, Sydney, Australia

3. College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China

4. M3S Empirical Software Engineering Research Unit, University of Oulu, Oulu, Finland

5. College of Computer Science and Technology, China University of Petroleum East China - Qingdao Campus, Qingdao, China

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Reference71 articles.

1. Latif U Khan, Ibrar Yaqoob, Muhammad Imran, Zhu Han, and Choong Seon Hong. 2020. 6G wireless systems: A vision, architectural elements, and future directions. IEEE access 8(2020), 147029–147044.

2. Fog Computing for Sustainable Smart Cities

3. Smart cities survey: Technologies, application domains and challenges for the cities of the future

4. Adaptive Segmentation Enhanced Asynchronous Federated Learning for Sustainable Intelligent Transportation Systems

5. Hugh Boyes, Bil Hallaq, Joe Cunningham, and Tim Watson. 2018. The industrial internet of things (IIoT): An analysis framework. Computers in industry 101 (2018), 1–12.

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