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[2602.22633] Tackling Privacy Heterogeneity in Differentially Private Federated Learning
Summary
This article presents a novel approach to address privacy heterogeneity in differentially private federated learning (DP-FL), proposing a privacy-aware client selection strategy that enhances model accuracy.
Why It Matters
As federated learning becomes more prevalent, understanding and addressing privacy heterogeneity is crucial for developing effective machine learning models. This research provides a theoretical foundation and practical solutions that can improve model performance while respecting diverse privacy requirements, making it highly relevant for researchers and practitioners in the field.
Key Takeaways
- Existing DP-FL methods often assume uniform privacy budgets, which is unrealistic.
- The proposed privacy-aware client selection strategy improves model accuracy by up to 10%.
- A theoretical convergence analysis quantifies the impact of privacy heterogeneity on training error.
- The study highlights the need for adaptive strategies in federated learning.
- Incorporating privacy heterogeneity can lead to more practical and effective federated learning applications.
Computer Science > Machine Learning arXiv:2602.22633 (cs) [Submitted on 26 Feb 2026] Title:Tackling Privacy Heterogeneity in Differentially Private Federated Learning Authors:Ruichen Xu, Ying-Jun Angela Zhang, Jianwei Huang View a PDF of the paper titled Tackling Privacy Heterogeneity in Differentially Private Federated Learning, by Ruichen Xu and 2 other authors View PDF HTML (experimental) Abstract:Differentially private federated learning (DP-FL) enables clients to collaboratively train machine learning models while preserving the privacy of their local data. However, most existing DP-FL approaches assume that all clients share a uniform privacy budget, an assumption that does not hold in real-world scenarios where privacy requirements vary widely. This privacy heterogeneity poses a significant challenge: conventional client selection strategies, which typically rely on data quantity, cannot distinguish between clients providing high-quality updates and those introducing substantial noise due to strict privacy constraints. To address this gap, we present the first systematic study of privacy-aware client selection in DP-FL. We establish a theoretical foundation by deriving a convergence analysis that quantifies the impact of privacy heterogeneity on training error. Building on this analysis, we propose a privacy-aware client selection strategy, formulated as a convex optimization problem, that adaptively adjusts selection probabilities to minimize training error. Extens...
