# A Scalable Approach for Privacy-Preserving Collaborative Machine Learning

arXiv: https://arxiv.org/abs/2011.01963 · HTML: https://mlsanity.com/paper/2011.01963/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 21 of 21)

- [What Do We Mean by Generalization in Federated Learning?](https://mlsanity.com/paper/2110.14216/) (2021)
- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards](https://mlsanity.com/paper/2112.09327/) (2021)
- [DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing](https://mlsanity.com/paper/2210.02680/) (2022)
- [Machine Learning with Confidential Computing: A Systematization of Knowledge](https://mlsanity.com/paper/2208.10134/) (2022)
- [Byzantine-Robust and Privacy-Preserving Framework for FedML](https://mlsanity.com/paper/2105.02295/) (2021)
- [Bounding Information Leakage in Machine Learning](https://mlsanity.com/paper/2105.03875/) (2021)
- [On the Tradeoff between Privacy Preservation and Byzantine-Robustness in Decentralized Learning](https://mlsanity.com/paper/2308.14606/) (2023)
- [ApproxIFER: A Model-Agnostic Approach to Resilient and Robust Prediction Serving Systems](https://mlsanity.com/paper/2109.09868/) (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)
- [General Coded Computing: Adversarial Settings](https://mlsanity.com/paper/2502.08058/) (2025)
- [FedMCSA: Personalized Federated Learning via Model Components Self-Attention](https://mlsanity.com/paper/2208.10731/) (2022)
- [Privacy-Preserving Federated Learning via System Immersion and Random Matrix Encryption](https://mlsanity.com/paper/2204.02497/) (2022)
- [Towards Industrial Private AI: A two-tier framework for data and model security](https://mlsanity.com/paper/2107.12806/) (2021)
- [Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation](https://mlsanity.com/paper/2204.11168/) (2022)
- [NeRCC: Nested-Regression Coded Computing for Resilient Distributed Prediction Serving Systems](https://mlsanity.com/paper/2402.04377/) (2024)
- [ImprovDML: Improved Trade-off in Private Byzantine-Resilient Distributed Machine Learning](https://mlsanity.com/paper/2506.15181/) (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)

## Abstract (arXiv)

We consider a collaborative learning scenario in which multiple data-owners wish to jointly train a logistic regression model, while keeping their individual datasets private from the other parties. We propose COPML, a fully-decentralized training framework that achieves scalability and privacy-protection simultaneously. The key idea of COPML is to securely encode the individual datasets to distribute the computation load effectively across many parties and to perform the training computations as well as the model updates in a distributed manner on the securely encoded data. We provide the privacy analysis of COPML and prove its convergence. Furthermore, we experimentally demonstrate that COPML can achieve significant speedup in training over the benchmark protocols. Our protocol provides strong statistical privacy guarantees against colluding parties (adversaries) with unbounded computational power, while achieving up to 16× speedup in the training time against the benchmark protocols.