How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
Open accessAbstract
This article first 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.
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In FL systems, there may exist free-riders who aim to benefit from the global model but do not want to contribute any useful information, thus compromising collaborative fairness.
“In FL systems, there may exist free-riders, who aim to benefit from the global model, but do not want to contribute any useful information, thus compromising collaborative fairness [167], [168], [184].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [184]
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- Authors
- Lyu, L., Li, Y., Nandakumar, K., Yu, J., & Ma, X.
- Affiliations
- National University of Singapore (SG)
- The University of Melbourne (AU)
- IBM (United States) (US)
- Monash University (AU)
- Deakin University (AU)
- Venue
- IEEE Transactions on Dependable and Secure Computing
- Published
- 2020
- Pages
- 1-1
- ISSN
- 1545-5971 (ISSN-L)
- 1941-0018
- 2160-9209
- OpenAlex
- W3039812354
- Cited by
- 31
- References
- 34
- Citations by year
- 2025: 1 · 2024: 12 · 2023: 3 · 2022: 9 · 2021: 4 · 2020: 2
- FWCI
- 1.77
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Dependable and Secure Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Faculty of Information Technology Monash University (Academic funding)
- IBM (IBM PhD Fellowship)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- federated deep learning, differential privacy, fairness, reputation systems, digital tokens, collaborative deep learning, privacy-preserving machine learning
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