How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
This addresses the challenge of democratizing AI by enabling fair and private decentralized learning for multiple parties, though it is incremental as it builds on existing differential privacy and fairness techniques.
The paper tackles the problem of ensuring fairness and privacy in collaborative deep learning by proposing a framework called FDPDDL, which uses a reputation system with digital tokens and differential privacy to achieve high fairness and comparable accuracy to centralized methods.
This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy. A novel reputation system is proposed through digital tokens and local credibility to ensure fairness, in combination with differential privacy to guarantee privacy. In particular, we build a fair and differentially private decentralised deep learning framework called FDPDDL, which enables parties to derive more accurate local models in a fair and private manner by using our developed two-stage scheme: during the initialisation stage, artificial samples generated by Differentially Private Generative Adversarial Network (DPGAN) are used to mutually benchmark the local credibility of each party and generate initial tokens; during the update stage, Differentially Private SGD (DPSGD) is used to facilitate collaborative privacy-preserving deep learning, and local credibility and tokens of each party are updated according to the quality and quantity of individually released gradients. Experimental results on benchmark datasets under three realistic settings demonstrate that FDPDDL achieves high fairness, yields comparable accuracy to the centralised and distributed frameworks, and delivers better accuracy than the standalone framework.