Automatic Double-Auction Mechanism for Federated Learning Service Market in Internet of Things

Abstract

In the past decade, the Artificial Intelligent & Internet of Things (AIoT) has become the disruptive force reshaping our lives and works. Billions of AIoT devices around the world are now connected to the Internet. This widespread deployment of AIoT devices presents huge challenges for the centralized AI training architecture (e.g., security issues, privacy issues). Recently, with the advance of federated learning (FL) technology, the integration of AIoT and FL is considered as a promising solution. FL enables distributed devices to collaboratively train a shared AI model while keeping all the training data contained in local. However, unlike the centralized architecture, the distributed learners are independent, uncontrollable, and self-interested. Designing effective incentive schemes to stimulate the distributed devices to actively participate in training tasks and contribute high-quality training models is the priority question. In this paper, we design a double auction mechanism for FL service market, where the trained models can be automatically traded between AIoT devices and FL platforms. Then, we propose an iterative double auction (IDA) algorithm, where a controller is implemented to guide participants to adjust their policies for the sake of maximizing individual participates utilities as well as social welfare. In addition, we propose a reinforcement learning-based scheme, termed as the Experience Weighted Attraction Learning-Double Auction (EWA-DA) algorithm. In EWA-DA, the participants can learn to improve their strategies directly by participating in the auction continuously, without the controller participating.