# How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning

arXiv: https://arxiv.org/abs/2007.09370 · HTML: https://mlsanity.com/paper/2007.09370/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2020.

## Built on by (most cited first, 12 of 12)

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- [The Impact of Differential Privacy on Group Disparity Mitigation](https://mlsanity.com/paper/2203.02745/) (2022)
- [Incentives in Private Collaborative Machine Learning](https://mlsanity.com/paper/2404.01676/) (2024)
- [BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning](https://mlsanity.com/paper/2202.02817/) (2022)
- [Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms](https://mlsanity.com/paper/2207.02337/) (2022)
- [FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning](https://mlsanity.com/paper/2405.18291/) (2024)
- [Contribution Evaluation in Federated Learning: Examining Current Approaches](https://mlsanity.com/paper/2311.09856/) (2023)
- [Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data](https://mlsanity.com/paper/2402.04375/) (2024)
- [Data Quality in Edge Machine Learning: A State-of-the-Art Survey](https://mlsanity.com/paper/2406.02600/) (2024)
- [ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoning](https://mlsanity.com/paper/2505.16850/) (2025)

## Abstract (arXiv)

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.