mlsanityData to 7 Oct 2026

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

arXiv 1902.00641 ↗First posted 2 Feb 2019cs.LG cs.CR cs.ITcited by 20

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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).

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Feb 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 5; 2023: 1; 2024: 1; 2025: 2; 2026: 3.

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  1. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
  2. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  3. 2021Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detectioncited by 19
  4. 2022DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharingcited by 17
  5. 2021Sparsified Secure Aggregation for Privacy-Preserving Federated Learningcited by 8
  6. 2024Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Frameworkcited by 5
  7. 2021List-Decodable Coded Computing: Breaking the Adversarial Toleration Barriercited by 4
  8. 2021Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learningcited by 4
  9. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  10. 2025General Coded Computing: Adversarial Settingscited by 4
  11. 2022MPClan: Protocol Suite for Privacy-Conscious Computationscited by 1
  12. 2022Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learningcited by 1
8 more
  1. 2021Privacy and Integrity Preserving Training Using Trusted Hardwarecited by 0
  2. 2021Canoe : A System for Collaborative Learning for Neural Netscited by 0
  3. 2022Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computationcited by 0
  4. 2023Iterative Sketching for Secure Coded Regressioncited by 0
  5. 2025DReS: Dual Reconstruction Smoothing for Functional Regularizationcited by 0
  6. 2026Learning-Theoretic Foundation for General Coded Computing: The Straggler Settingcited by 0
  7. 2026Manifold-Aware General Coded Computing for Straggler-Resilient Distributed Computingcited by 0
  8. 2026GenomeOcean Anywhere: Private WebGPU Inference for Genome MoEscited by 0