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Sikha Pentyala

Alumni

Publications

Privacy-Preserving Group Fairness in Cross-Device Federated Learning
Sikha Pentyala
Nicola Neophytou
Anderson Nascimento
Martine De Cock
Golnoosh Farnadi
Group fairness ensures that the outcome of machine learning (ML) based decision making systems are notbiased towards a certain group of peop… (voir plus)le defined by a sensitive attribute such as gender or ethnicity. Achievinggroup fairness in Federated Learning (FL) is challenging because mitigating bias inherently requires usingthe sensitive attribute values of all clients, while FL is aimed precisely at protecting privacy by not givingaccess to the clients’ data. As we show in this paper, this conflict between fairness and privacy in FL can beresolved by combining FL with Secure Multiparty Computation (MPC) and Differential Privacy (DP). Tothis end, we propose a privacy-preserving approach to calculate group fairness notions in the cross-device FLsetting. Then, we propose two bias mitigation pre-processing and post-processing techniques in cross-deviceFL under formal privacy guarantees, without requiring the clients to disclose their sensitive attribute values.Empirical evaluations on real world datasets demonstrate the effectiveness of our solution to train fair andaccurate ML models in federated cross-device setups with privacy guarantees to the users.
2025-04-23
Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (publié)
proceedings.mlr.press
Privacy-Preserving Fair Item Ranking
Jiajun Sun
Sikha Pentyala
Martine De Cock
Golnoosh Farnadi
Users worldwide access massive amounts of curated data in the form of rankings on a daily basis. The societal impact of this ease of access … (voir plus)has been studied and work has been done to propose and enforce various notions of fairness in rankings. Current computational methods for fair item ranking rely on disclosing user data to a centralized server, which gives rise to privacy concerns for the users. This work is the first to advance research at the conjunction of producer (item) fairness and consumer (user) privacy in rankings by exploring the incorporation of privacy-preserving techniques; specifically, differential privacy and secure multi-party computation. Our work extends the equity of amortized attention ranking mechanism to be privacy-preserving, and we evaluate its effects with respect to privacy, fairness, and ranking quality. Our results using real-world datasets show that we are able to effectively preserve the privacy of users and mitigate unfairness of items without making additional sacrifices to the quality of rankings in comparison to the ranking mechanism in the clear.
2023-03-06
ArXiv (prépublication)
doi.org
arxiv.org