# CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning

arXiv: https://arxiv.org/abs/1902.00641 · HTML: https://mlsanity.com/paper/1902.00641/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2019.

## Built on by (most cited first, 20 of 20)

- [FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks](https://mlsanity.com/paper/2104.08815/) (2021)
- [LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2109.14236/) (2021)
- [Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection](https://mlsanity.com/paper/2106.07976/) (2021)
- [DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing](https://mlsanity.com/paper/2210.02680/) (2022)
- [Sparsified Secure Aggregation for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2112.12872/) (2021)
- [Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework](https://mlsanity.com/paper/2406.00300/) (2024)
- [List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier](https://mlsanity.com/paper/2101.11653/) (2021)
- [Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning](https://mlsanity.com/paper/2107.12958/) (2021)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [General Coded Computing: Adversarial Settings](https://mlsanity.com/paper/2502.08058/) (2025)
- [MPClan: Protocol Suite for Privacy-Conscious Computations](https://mlsanity.com/paper/2206.12224/) (2022)
- [Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning](https://mlsanity.com/paper/2209.12046/) (2022)
- [Privacy and Integrity Preserving Training Using Trusted Hardware](https://mlsanity.com/paper/2105.00334/) (2021)
- [Canoe : A System for Collaborative Learning for Neural Nets](https://mlsanity.com/paper/2108.12124/) (2021)
- [Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation](https://mlsanity.com/paper/2204.11168/) (2022)
- [Iterative Sketching for Secure Coded Regression](https://mlsanity.com/paper/2308.04185/) (2023)
- [DReS: Dual Reconstruction Smoothing for Functional Regularization](https://mlsanity.com/paper/2510.00253/) (2025)
- [Learning-Theoretic Foundation for General Coded Computing: The Straggler Setting](https://mlsanity.com/paper/2608.28910/) (2026)
- [Manifold-Aware General Coded Computing for Straggler-Resilient Distributed Computing](https://mlsanity.com/paper/2609.00552/) (2026)
- [GenomeOcean Anywhere: Private WebGPU Inference for Genome MoEs](https://mlsanity.com/paper/2609.35882/) (2026)

## Abstract (arXiv)

How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers. We characterize CodedPrivateML's privacy threshold and prove its convergence for logistic (and linear) regression. Furthermore, via extensive experiments on Amazon EC2, we demonstrate that CodedPrivateML provides significant speedup over cryptographic approaches based on multi-party computing (MPC).