Resource sharing for collaborative edge learning: A privacy-aware incentive mechanism combined with demand prediction

Abstract

Edge intelligence benefits from ubiquitous resource capabilities by numerous edge devices. However, interest concerns and privacy issues caused by the openness of the edge network make smart devices reluctant to participate in collaborative edge learning. This work proposes a privacy-aware incentive mechanism based on combinatorial double auction (PIMCDA) to facilitate idle resource sharing of diversified edge devices. First, we establish an auction model based on blockchain to form a trusted sharing market in a multi-resource binding manner. Then, we propose a two-stage auction solution combined with differential privacy: The privacy-aware winner selection stage matches the optimal winner candidate to the task that is the closest in probability to maximize the revenue of resource providers with the obfuscated location information. The probabilistic pricing decision stage is designed based on a uniform pricing method and exponential mechanism, which can ensure bid privacy from inference attacks. Furthermore, to ensure the effectiveness of PIMCDA for long-term participation, we design a supply and demand balance mechanism with a learning-based resource prediction method. Theoretical and simulation analysis demonstrate that the proposed mechanism achieves privacy-preserving in location and bid information while ensuring effective incentive properties. Our approach effectively motivates the sharing of multiple resources in edge computing.