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• W4328006298 abstract "Collaborative inference enables mobile devices to offload the computation tasks and support computation-intensive perception services such as object detection with lower latency and higher energy efficiency. In the deep learning based perception in mobile edge computing (MEC), each mobile device has to choose the computation partition and the collaborative edge server. However, the optimal partition point and edge selection depend on the inference cost model of the specific deep learning architecture and the channel models from devices to edge servers, which are challenging to formulate in practical MEC. In this paper, we propose a multi-agent reinforcement learning based energy-efficient collaborative inference scheme for MEC to support perception services. The inference scheme applies multi-agent Q-learning to choose the partition point of the deep learning model and the collaborative edge server based on the state that consists of the task workload, the channel conditions, and the inference cost record. A Q-network on the central server approximates the long-term expected utilities or global Q-values under the states and policies of all mobile devices, which is the direction for each mobile device to update the Q-value under the local state-policy pair. Simulation results show that our proposed scheme reduces the inference latency and saves the total energy consumption for MEC." @default.
• W4328006298 created "2023-03-22" @default.
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• W4328006298 date "2022-08-01" @default.
• W4328006298 modified "2023-09-26" @default.
• W4328006298 title "Energy-Efficient Collaborative Inference in MEC: A Multi-Agent Reinforcement Learning Based Approach" @default.
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• W4328006298 doi "https://doi.org/10.1109/bigcom57025.2022.00057" @default.
• W4328006298 hasPublicationYear "2022" @default.
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