# Communication-Efficient Distributed Learning with Differential Privacy

arXiv: https://arxiv.org/abs/2604.02558 · HTML: https://mlsanity.com/paper/2604.02558/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 16 of 16)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [Subsampled R'enyi Differential Privacy and Analytical Moments Accountant](https://mlsanity.com/paper/1808.00087/) (2018)
- [Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations](https://mlsanity.com/paper/1906.02367/) (2019)
- [Differentially Private Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2111.09278/) (2021)
- [Local Exact-Diffusion for Decentralized Optimization and Learning](https://mlsanity.com/paper/2302.00620/) (2023)
- [Innovation Compression for Communication-efficient Distributed Optimization with Linear Convergence](https://mlsanity.com/paper/2105.06697/) (2021)
- [Separation of Learning and Control for Cyber-Physical Systems](https://mlsanity.com/paper/2107.06379/) (2021)
- [CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence](https://mlsanity.com/paper/2301.05872/) (2023)
- [Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression](https://mlsanity.com/paper/2305.09896/) (2023)
- [Multi-Robot Cooperative Navigation in Crowds: A Game-Theoretic Learning-Based Model Predictive Control Approach](https://mlsanity.com/paper/2310.06964/) (2023)
- [A survey on secure decentralized optimization and learning](https://mlsanity.com/paper/2408.08628/) (2024)
- [On the Output Redundancy of LTI Systems: A Geometric Approach with Application to Privacy](https://mlsanity.com/paper/2409.17705/) (2024)
- [Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming](https://mlsanity.com/paper/2503.21548/) (2025)
- [Jointly Computation- and Communication-Efficient Distributed Learning](https://mlsanity.com/paper/2508.15509/) (2025)

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

- [SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM](https://mlsanity.com/paper/2609.29446/) (2026)

## Abstract (arXiv)

We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that is both communication-efficient and that guarantees the privacy of the agents' data. The first goal is achieved through a local training approach, which reduces communication frequency. The second goal is achieved by perturbing gradients during local training, specifically through gradient clipping and additive noise. We prove that the resulting algorithm converges to a stationary point of the problem within a bounded distance. Additionally, we provide theoretical privacy guarantees within a differential privacy framework that ensure agents' training data cannot be inferred from the trained model shared over the network. We show the algorithm's superior performance on a classification task under the same privacy budget, compared with state-of-the-art methods.