# LEASGD: an Efficient and Privacy-Preserving Decentralized Algorithm for Distributed Learning

arXiv: https://arxiv.org/abs/1811.11124 · HTML: https://mlsanity.com/paper/1811.11124/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy](https://mlsanity.com/paper/2203.06580/) (2022)
- [Topology-Based Reconstruction Prevention for Decentralised Learning](https://mlsanity.com/paper/2312.05248/) (2023)
- [PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds](https://mlsanity.com/paper/2405.02638/) (2024)
- [A Privacy-Preserving and Trustable Multi-agent Learning Framework](https://mlsanity.com/paper/2106.01242/) (2021)
- [DP-CSGP: Differentially Private Stochastic Gradient Push with Compressed Communication](https://mlsanity.com/paper/2512.13583/) (2025)
- [Dyn-D2P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee](https://mlsanity.com/paper/2505.06651/) (2025)

## Abstract (arXiv)

Distributed learning systems have enabled training large-scale models over large amount of data in significantly shorter time. In this paper, we focus on decentralized distributed deep learning systems and aim to achieve differential privacy with good convergence rate and low communication cost. To achieve this goal, we propose a new learning algorithm LEASGD (Leader-Follower Elastic Averaging Stochastic Gradient Descent), which is driven by a novel Leader-Follower topology and a differential privacy model.We provide a theoretical analysis of the convergence rate and the trade-off between the performance and privacy in the private setting.The experimental results show that LEASGD outperforms state-of-the-art decentralized learning algorithm DPSGD by achieving steadily lower loss within the same iterations and by reducing the communication cost by 30%. In addition, LEASGD spends less differential privacy budget and has higher final accuracy result than DPSGD under private setting.