mlsanityData to 14 Oct 2026

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

arXiv 2412.21069 ↗First posted 30 Dec 2024eess.SY cs.LG cs.NIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 19 references that are in the map. First posted on arXiv 30 Dec 2024.

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References · 2

  1. 2024Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approachcited by 6
  2. 2024MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defensecited by 5

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