First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions

• URL: http://arxiv.org/abs/2512.21521v1
• Date: Thu, 25 Dec 2025 06:05:15 GMT
• Title: First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions
• Authors: Egor Shulgin, Grigory Malinovsky, Sarit Khirirat, Peter Richtárik,
• Abstract summary: Fed-$$-NormEC is the first differentially private FL framework providing provable convergence and DP guarantees under standard assumptions.<n>Fed-$$-NormE integrates local updates, separate server and client stepsizes, and, crucially, partial client participation.
• Score: 52.82254388526969
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) enables collaborative training on decentralized data. Differential privacy (DP) is crucial for FL, but current private methods often rely on unrealistic assumptions (e.g., bounded gradients or heterogeneity), hindering practical application. Existing works that relax these assumptions typically neglect practical FL features, including multiple local updates and partial client participation. We introduce Fed-$α$-NormEC, the first differentially private FL framework providing provable convergence and DP guarantees under standard assumptions while fully supporting these practical features. Fed-$α$-NormE integrates local updates (full and incremental gradient steps), separate server and client stepsizes, and, crucially, partial client participation, which is essential for real-world deployment and vital for privacy amplification. Our theoretical guarantees are corroborated by experiments on private deep learning tasks.

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