# Learning Privately over Distributed Features: An ADMM Sharing Approach

arXiv: https://arxiv.org/abs/1907.07735 · HTML: https://mlsanity.com/paper/1907.07735/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 14 of 14)

- [Practical Vertical Federated Learning with Unsupervised Representation Learning](https://mlsanity.com/paper/2208.10278/) (2022)
- [Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning](https://mlsanity.com/paper/2112.05409/) (2021)
- [Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples](https://mlsanity.com/paper/2303.16270/) (2023)
- [Self-supervised Cross-silo Federated Neural Architecture Search](https://mlsanity.com/paper/2101.11896/) (2021)
- [A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning](https://mlsanity.com/paper/2106.06312/) (2021)
- [Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM](https://mlsanity.com/paper/2207.10226/) (2022)
- [Differentially Private Vertical Federated Clustering](https://mlsanity.com/paper/2208.01700/) (2022)
- [Robust and IP-Protecting Vertical Federated Learning against Unexpected Quitting of Parties](https://mlsanity.com/paper/2303.18178/) (2023)
- [From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning](https://mlsanity.com/paper/2302.12559/) (2023)
- [FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation](https://mlsanity.com/paper/2312.00102/) (2023)
- [Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings](https://mlsanity.com/paper/2308.02362/) (2023)
- [Private Networked Federated Learning for Nonsmooth Objectives](https://mlsanity.com/paper/2306.14012/) (2023)
- [TablePuppet: A Generic Framework for Relational Federated Learning](https://mlsanity.com/paper/2403.15839/) (2024)
- [Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation](https://mlsanity.com/paper/2602.15996/) (2026)

## Abstract (arXiv)

Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem where features are inherently distributed or vertically partitioned among multiple parties, and sharing of raw data or model parameters among parties is prohibited due to privacy concerns. We propose an ADMM sharing framework to approach risk minimization over distributed features, where each party only needs to share a single value for each sample in the training process, thus minimizing the data leakage risk. We establish convergence and iteration complexity results for the proposed parallel ADMM algorithm under non-convex loss. We further introduce a novel differentially private ADMM sharing algorithm and bound the privacy guarantee with carefully designed noise perturbation. The experiments based on a prototype system shows that the proposed ADMM algorithms converge efficiently in a robust fashion, demonstrating advantage over gradient based methods especially for data set with high dimensional feature spaces.