Privacy-Aware Multi-Device Cooperative Edge Inference with Distributed Resource Bidding
Wenhao Zhuang, Yuyi Mao

TL;DR
This paper presents a privacy-aware multi-device cooperative edge inference system that uses distributed resource bidding and feature compression, optimizing classification accuracy while protecting data privacy in mobile edge computing environments.
Contribution
It introduces a decentralized bidding mechanism and a MADDPG-based algorithm for privacy-preserving cooperative AI inference at the edge.
Findings
Achieves 0.31-0.95% accuracy improvement with privacy protection.
Enhances performance by 1.54-1.67% considering inference data challenges.
Effectively balances privacy and inference accuracy in MEC settings.
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.…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Stochastic Gradient Optimization Techniques · IoT and Edge/Fog Computing
