# Privacy-Preserving Distributed Optimization and Learning

arXiv: https://arxiv.org/abs/2403.00157 · HTML: https://mlsanity.com/paper/2403.00157/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 90 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 29 of 29)

- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Push-Pull Gradient Methods for Distributed Optimization in Networks](https://mlsanity.com/paper/1810.06653/) (2018)
- [DP-ADMM: ADMM-based Distributed Learning with Differential Privacy](https://mlsanity.com/paper/1808.10101/) (2018)
- [Differentially Private Distributed Optimization](https://mlsanity.com/paper/1401.2596/) (2014)
- [Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms](https://mlsanity.com/paper/1806.02246/) (2018)
- [Tailoring Gradient Methods for Differentially-Private Distributed Optimization](https://mlsanity.com/paper/2202.01113/) (2022)
- [Differentially Private Distributed Online Learning](https://mlsanity.com/paper/1505.06556/) (2015)
- [Differentially Private Distributed Constrained Optimization](https://mlsanity.com/paper/1411.4105/) (2014)
- [Differentially Private Distributed Convex Optimization via Functional Perturbation](https://mlsanity.com/paper/1512.00369/) (2015)
- [Decentralized Nonconvex Optimization with Guaranteed Privacy and Accuracy](https://mlsanity.com/paper/2212.07534/) (2022)
- [An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization](https://mlsanity.com/paper/2105.05717/) (2021)
- [Privacy Preservation in Distributed Subgradient Optimization Algorithms](https://mlsanity.com/paper/1512.08822/) (2015)
- [Quantization enabled Privacy Protection in Decentralized Stochastic Optimization](https://mlsanity.com/paper/2208.04845/) (2022)
- [Privacy preserving distributed optimization using homomorphic encryption](https://mlsanity.com/paper/1805.00572/) (2018)
- [Topology-aware Differential Privacy for Decentralized Image Classification](https://mlsanity.com/paper/2006.07817/) (2020)
- [Decentralized Gradient Methods with Time-varying Uncoordinated Stepsizes: Convergence Analysis and Privacy Design](https://mlsanity.com/paper/2205.10934/) (2022)
- [Dynamics based Privacy Preservation in Decentralized Optimization](https://mlsanity.com/paper/2207.05350/) (2022)
- [Gradient-tracking Based Differentially Private Distributed Optimization with Enhanced Optimization Accuracy](https://mlsanity.com/paper/2212.05364/) (2022)
- [Distributed Empirical Risk Minimization With Differential Privacy](https://mlsanity.com/paper/2302.10520/) (2023)
- [Cloud-based Quadratic Optimization with Partially Homomorphic Encryption](https://mlsanity.com/paper/1809.02267/) (2018)
- [Distributed Online Private Learning of Convex Nondecomposable Objectives](https://mlsanity.com/paper/2206.07944/) (2022)
- [Differentially-private Distributed Algorithms for Aggregative Games with Guaranteed Convergence](https://mlsanity.com/paper/2209.01486/) (2022)
- [Privacy-preserving Decentralized Optimization via Decomposition](https://mlsanity.com/paper/1808.09566/) (2018)
- [On Privatizing Equilibrium Computation in Aggregate Games over Networks](https://mlsanity.com/paper/1912.06296/) (2019)
- [Preserving Statistical Privacy in Distributed Optimization](https://mlsanity.com/paper/2004.01312/) (2020)
- [Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs](https://mlsanity.com/paper/2310.16105/) (2023)
- [Cloud-Enabled Differentially Private Multi-Agent Optimization with Constraints](https://mlsanity.com/paper/1507.04371/) (2015)
- [Private and Robust Distributed Nonconvex Optimization via Polynomial Approximation](https://mlsanity.com/paper/2101.06127/) (2021)
- [Distributed Privacy-Preserving Electric Vehicle Charging Control Based on Secret Sharing](https://mlsanity.com/paper/2110.02280/) (2021)

## Built on by (most cited first, 2 of 2)

- [A survey on secure decentralized optimization and learning](https://mlsanity.com/paper/2408.08628/) (2024)
- [Learning to accelerate distributed ADMM using graph neural networks](https://mlsanity.com/paper/2509.05288/) (2025)

## Abstract (arXiv)

Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite rapid development, existing distributed optimization and learning algorithms require each agent to exchange messages with its neighbors, which may expose sensitive information and raise significant privacy concerns. In this survey paper, we overview privacy-preserving distributed optimization and learning methods. We first discuss cryptography, differential privacy, and other techniques that can be used for privacy preservation and indicate their pros and cons for privacy protection in distributed optimization and learning. We believe that among these approaches, differential privacy is most promising due to its low computational and communication complexities, which are extremely appealing for modern learning based applications with high dimensions of optimization variables. We then introduce several differential-privacy algorithms that can simultaneously ensure privacy and optimization accuracy. Moreover, we provide example applications in several machine learning problems to confirm the real-world effectiveness of these algorithms. Finally, we highlight some challenges in this research domain and discuss future directions.