mlsanityData to 7 Oct 2026

Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems

arXiv 2407.12396 ↗First posted 17 Jul 2024cs.LGcited by 3

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Abstract (arXiv)

This paper addresses the challenge of preserving privacy in Federated Learning (FL) within centralized systems, focusing on both trusted and untrusted server scenarios. We analyze this setting within the Stochastic Convex Optimization (SCO) framework, and devise methods that ensure Differential Privacy (DP) while maintaining optimal convergence rates for homogeneous and heterogeneous data distributions. Our approach, based on a recent stochastic optimization technique, offers linear computational complexity, comparable to non-private FL methods, and reduced gradient obfuscation. This work enhances the practicality of DP in FL, balancing privacy, efficiency, and robustness in a variety of server trust environment.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 17 Jul 2024.

Builds on

19 of 39 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2016Deep Learning with Differential Privacycited by 2,682
  4. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  5. 2019Momentum-Based Variance Reduction in Non-Convex SGDcited by 302
  6. 2019Private Stochastic Convex Optimization with Optimal Ratescited by 183
  7. 2021Practical and Private (Deep) Learning without Sampling or Shufflingcited by 169
  8. 2020Private Stochastic Convex Optimization: Optimal Rates in Linear Timecited by 152
  9. 2018Differentially Private Empirical Risk Minimization Revisited: Faster and More Generalcited by 127
  10. 2021Differentially Private Federated Learning on Heterogeneous Datacited by 74
  11. 2019UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimizationcited by 56
  12. 2018DP-ADMM: ADMM-based Distributed Learning with Differential Privacycited by 41
7 more
  1. 2021Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Lossescited by 30
  2. 2021Differentially private inference via noisy optimizationcited by 27
  3. 2022Private Non-Convex Federated Learning Without a Trusted Servercited by 26
  4. 2014Private Multiplicative Weights Beyond Linear Queriescited by 17
  5. 2021Shuffle Private Stochastic Convex Optimizationcited by 17
  6. 2023Faster Differentially Private Convex Optimization via Second-Order Methodscited by 13
  7. 2023μ2-SGD: Stable Stochastic Optimization via a Double Momentum Mechanismcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellationcited by 1
  2. 2026Bringing Order to Asynchronous SGD: Towards Optimality under Data-Dependent Delays with Momentumcited by 0
  3. 2026Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustnesscited by 0