# Enforcing Privacy in Distributed Learning with Performance Guarantees

arXiv: https://arxiv.org/abs/2301.06412 · HTML: https://mlsanity.com/paper/2301.06412/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 16 Jan 2023.

## Builds on (most cited first, 17 of 17)

- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [LDP-Fed: Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2006.03637/) (2020)
- [Federated Learning with Bayesian Differential Privacy](https://mlsanity.com/paper/1911.10071/) (2019)
- [Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning](https://mlsanity.com/paper/2001.02112/) (2020)
- [Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate](https://mlsanity.com/paper/1907.01848/) (2019)
- [On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis](https://mlsanity.com/paper/1312.7581/) (2013)
- [Differentially Private Distributed Online Learning](https://mlsanity.com/paper/1505.06556/) (2015)
- [Decentralized Stochastic Optimization with Inherent Privacy Protection](https://mlsanity.com/paper/2205.03884/) (2022)
- [Scalable Privacy-Preserving Distributed Learning](https://mlsanity.com/paper/2005.09532/) (2020)
- [Graph-Homomorphic Perturbations for Private Decentralized Learning](https://mlsanity.com/paper/2010.12288/) (2020)
- [Performance Limits of Stochastic Sub-Gradient Learning, Part II: Multi-Agent Case](https://mlsanity.com/paper/1704.06025/) (2017)

## Built on by (most cited first, 3 of 3)

- [Differentially Private Distributed Estimation and Learning](https://mlsanity.com/paper/2306.15865/) (2023)
- [On the Escaping Efficiency of Distributed Adversarial Training Algorithms](https://mlsanity.com/paper/2509.11337/) (2025)
- [SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM](https://mlsanity.com/paper/2609.29446/) (2026)

## Abstract (arXiv)

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is not well-tuned to the graph structure. For this reason, we exploit two alternative graph-homomorphic constructions and show that they improve performance while guaranteeing privacy. Moreover, contrary to most earlier studies, the gradient of the risks is not assumed to be bounded (a condition that rarely holds in practice; e.g., quadratic risk). We avoid this condition and still devise a differentially private scheme with high probability. We examine optimization and learning scenarios and illustrate the theoretical findings through simulations.