Privacy-Aware Multi-Device Cooperative Edge Inference with Distributed Resource Bidding

• URL: http://arxiv.org/abs/2412.21069v1
• Date: Mon, 30 Dec 2024 16:37:17 GMT
• Title: Privacy-Aware Multi-Device Cooperative Edge Inference with Distributed Resource Bidding
• Authors: Wenhao Zhuang, Yuyi Mao,
• Abstract summary: Mobile edge computing (MEC) has empowered mobile devices (MDs) in supporting artificial intelligence (AI) applications. Despite the great promise of device-edge cooperative AI inference, data privacy becomes an increasing concern. We develop a privacy-aware multi-device cooperative edge inference system for classification tasks.
• Score: 3.9287497907611875
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• Abstract: Mobile edge computing (MEC) has empowered mobile devices (MDs) in supporting artificial intelligence (AI) applications through collaborative efforts with proximal MEC servers. Unfortunately, despite the great promise of device-edge cooperative AI inference, data privacy becomes an increasing concern. In this paper, we develop a privacy-aware multi-device cooperative edge inference system for classification tasks, which integrates a distributed bidding mechanism for the MEC server's computational resources. Intermediate feature compression is adopted as a principled approach to minimize data privacy leakage. To determine the bidding values and feature compression ratios in a distributed fashion, we formulate a decentralized partially observable Markov decision process (DEC-POMDP) model, for which, a multi-agent deep deterministic policy gradient (MADDPG)-based algorithm is developed. Simulation results demonstrate the effectiveness of the proposed algorithm in privacy-preserving cooperative edge inference. Specifically, given a sufficient level of data privacy protection, the proposed algorithm achieves 0.31-0.95% improvements in classification accuracy compared to the approach being agnostic to the wireless channel conditions. The performance is further enhanced by 1.54-1.67% by considering the difficulties of inference data.

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