# Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation

arXiv: https://arxiv.org/abs/2506.02563 · HTML: https://mlsanity.com/paper/2506.02563/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [Momentum-Based Variance Reduction in Non-Convex SGD](https://mlsanity.com/paper/1905.10018/) (2019)
- [Private Stochastic Convex Optimization with Optimal Rates](https://mlsanity.com/paper/1908.09970/) (2019)
- [Practical and Private (Deep) Learning without Sampling or Shuffling](https://mlsanity.com/paper/2103.00039/) (2021)
- [Private Stochastic Convex Optimization: Optimal Rates in Linear Time](https://mlsanity.com/paper/2005.04763/) (2020)
- [Differentially Private Empirical Risk Minimization Revisited: Faster and More General](https://mlsanity.com/paper/1802.05251/) (2018)
- [Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds](https://mlsanity.com/paper/1405.7085/) (2014)
- [Differentially Private Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2111.09278/) (2021)
- [UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization](https://mlsanity.com/paper/1910.13857/) (2019)
- [DP-ADMM: ADMM-based Distributed Learning with Differential Privacy](https://mlsanity.com/paper/1808.10101/) (2018)
- [Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses](https://mlsanity.com/paper/2106.09779/) (2021)
- [Differentially private inference via noisy optimization](https://mlsanity.com/paper/2103.11003/) (2021)
- [Private Non-Convex Federated Learning Without a Trusted Server](https://mlsanity.com/paper/2203.06735/) (2022)
- [Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy](https://mlsanity.com/paper/2302.01463/) (2023)
- [Private Multiplicative Weights Beyond Linear Queries](https://mlsanity.com/paper/1407.1571/) (2014)
- [Shuffle Private Stochastic Convex Optimization](https://mlsanity.com/paper/2106.09805/) (2021)
- [Faster Differentially Private Convex Optimization via Second-Order Methods](https://mlsanity.com/paper/2305.13209/) (2023)
- [Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses](https://mlsanity.com/paper/2407.09690/) (2024)
- [μ2-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism](https://mlsanity.com/paper/2304.04172/) (2023)
- [Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems](https://mlsanity.com/paper/2407.12396/) (2024)
- [DPNCT: A Differential Private Noise Cancellation Scheme for Load Monitoring and Billing for Smart Meters](https://mlsanity.com/paper/2102.09458/) (2021)

## Built on by (most cited first, 1 of 1)

- [Bringing Order to Asynchronous SGD: Towards Optimality under Data-Dependent Delays with Momentum](https://mlsanity.com/paper/2605.02043/) (2026)

## Abstract (arXiv)

This paper tackles the challenge of achieving Differential Privacy (DP) in Federated Learning (FL) under partial-participation, where only a subset of the machines participate in each time-step. While previous work achieved optimal performance in full-participation settings, these methods struggled to extend to partial-participation scenarios. Our approach fills this gap by introducing a novel noise-cancellation mechanism that preserves privacy without sacrificing convergence rates or computational efficiency. We analyze our method within the Stochastic Convex Optimization (SCO) framework and show that it delivers optimal performance for both homogeneous and heterogeneous data distributions. This work expands the applicability of DP in FL, offering an efficient and practical solution for privacy-preserving learning in distributed systems with partial participation.