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Multi-Agent Systems for Collaborative Inference Based on Deep Policy Q-Inference Network

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Abstract

This study tackles the problem of increasing efficiency and scalability in deep neural network (DNN) systems by employing collaborative inference, an approach that is gaining popularity because to its ability to maximize computational resources. It involves splitting a pre-trained DNN model into two parts and running them separately on user equipment (UE) and edge servers. This approach is advantageous because it results in faster and more energy-efficient inference, as computation can be offloaded to edge servers rather than relying solely on UEs. However, a significant challenge of collaborative belief is the dynamic coupling of DNN layers, which makes it difficult to separate and run the layers independently. To address this challenge, we proposed a novel approach to optimize collaborative inference in a multi-agent scenario where a single-edge server coordinates the assumption of multiple UEs. Our proposed method suggests using an autoencoder-based technique to reduce the size of intermediary features and constructing tasks using the deep policy inference Q-inference network’s overhead (DPIQN). To optimize the collaborative inference, employ the Deep Recurrent Policy Inference Q-Network (DRPIQN) technique, which allows for a hybrid action space. The results of the tests demonstrate that this approach can significantly reduce inference latency by up to 56% and energy usage by up to 72% on various networks. Overall, this proposed approach provides an efficient and effective method for implementing collaborative inference in multi-agent scenarios, which could have significant implications for developing DNN systems.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission of China. (Grant No. KJQN202004002). Also, this work was sponsored in part by Anhui Provincial Quality Engineering Project for Higher Education Institutions (2020-2023 "Logistics Facilities, Equipment, and Information Technology", the original high-quality offline open course (2020kfkc535).

Author information

Authors and Affiliations

  1. Academy of Engineering, Fundamental Informatics and Information Technology, People’s Friendship University of Russia, Moscow, 117198, Russia

    Shangshang Wang

  2. College of Electronical and Information Engineering, Chongqing Open University, Chongqing, 400052, China

    Yuqin Jing

  3. Department of Business Administration, Lu’an Vocational and Technical College, Lu’an, Anhui, 2370000, China

    Kezhu Wang

  4. School of Education Science, Nanjing Normal University, Nanjing, Jiangsu, 210097, China

    Xue Wang

  5. Smart Campus Management Office, Nanjing Audit University, Nanjing, Jiangsu, 211815, China

    Xue Wang

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  1. Shangshang Wang
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Contributions

ShangshangWang: Conceptualization, Methodology, Formal analysis, Supervision, Writing—original draft, Writing—review & editing.

Yuqin Jing: Writing—original draft, Writing—review & editing, Investigation, Data Curation, Validation, Resources, Writing—review & editing.

Kezhu Wang: Writing—original draft, Writing—review & editing, Investigation, Data Curation, Validation, Resources, Writing—review & editing.

Xue Wang: Investigation, Data Curation, Validation, Resources, Writing—review & editing.

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Correspondence to Xue Wang.

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Wang, S., Jing, Y., Wang, K. et al. Multi-Agent Systems for Collaborative Inference Based on Deep Policy Q-Inference Network. J Grid Computing 22, 38 (2024). https://doi.org/10.1007/s10723-024-09750-w

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