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PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning
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Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity. Achieving group fairness in Federated Learning (FL) is challenging because mitigating bias inherently requires using the sensitive attribute values of all clients, while FL is aimed precisely at protecting privacy by not giving access to the clients' data. As we show in this paper, this conflict between fairness and privacy in FL can be resolved by combining FL with Secure Multiparty Computation (MPC) and Differential Privacy (DP). In doing so, we propose a method for training group-fair ML models in cross-device FL under complete and formal privacy guarantees, without requiring the clients to disclose their sensitive attribute values.
Forward citations
Cited by 2 Pith papers
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Fairness in Federated Learning: Fairness for Whom?
A critical review of 121 federated learning fairness papers identifies five recurring pitfalls and proposes a harm-centered, lifecycle-based framework.
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The Fair Game: Auditing & Debiasing AI Algorithms Over Time
Proposes 'Fair Game', a reinforcement-learning loop in which an auditor's bias criteria, updatable over time, steer a debiasing agent that adapts an ML model's predictions.
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