# Contrastive Federated Learning with Tabular Data Silos

arXiv: https://arxiv.org/abs/2409.06123 · HTML: https://mlsanity.com/paper/2409.06123/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 59 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 26 of 26)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [The Non-IID Data Quagmire of Decentralized Machine Learning](https://mlsanity.com/paper/1910.00189/) (2019)
- [Contrastive Learning of Structured World Models](https://mlsanity.com/paper/1911.12247/) (2019)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Federated Learning for Emoji Prediction in a Mobile Keyboard](https://mlsanity.com/paper/1906.04329/) (2019)
- [SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning](https://mlsanity.com/paper/2110.04361/) (2021)
- [Federated Unsupervised Representation Learning](https://mlsanity.com/paper/2010.08982/) (2020)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023](https://mlsanity.com/paper/2307.14338/) (2023)
- [Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data](https://mlsanity.com/paper/2303.14080/) (2023)
- [Vertical Federated Learning: Challenges, Methodologies and Experiments](https://mlsanity.com/paper/2202.04309/) (2022)
- [Federated Learning Of Out-Of-Vocabulary Words](https://mlsanity.com/paper/1903.10635/) (2019)
- [FedCVT: Semi-supervised Vertical Federated Learning with Cross-view Training](https://mlsanity.com/paper/2008.10838/) (2020)
- [Implicit Gradient Alignment in Distributed and Federated Learning](https://mlsanity.com/paper/2106.13897/) (2021)
- [Triple Memory Networks: a Brain-Inspired Method for Continual Learning](https://mlsanity.com/paper/2003.03143/) (2020)
- [FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning](https://mlsanity.com/paper/2206.03200/) (2022)
- [Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data](https://mlsanity.com/paper/2105.04727/) (2021)
- [Masked Contrastive Representation Learning](https://mlsanity.com/paper/2211.06012/) (2022)
- [Data Augmentation for Sample Efficient and Robust Document Ranking](https://mlsanity.com/paper/2311.15426/) (2023)
- [BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space](https://mlsanity.com/paper/2206.10747/) (2022)

## Built on by (most cited first, 2 of 2)

- [Continual Contrastive Learning on Tabular Data with Out of Distribution](https://mlsanity.com/paper/2503.15089/) (2025)
- [Representation Learning on Out of Distribution in Tabular Data](https://mlsanity.com/paper/2502.10095/) (2025)

## Abstract (arXiv)

Learning from vertical partitioned data silos is challenging due to the segmented nature of data, sample misalignment, and strict privacy concerns. Federated learning has been proposed as a solution. However, sample misalignment across silos often hinders optimal model performance and suggests data sharing within the model, which breaks privacy. Our proposed solution is Contrastive Federated Learning with Tabular Data Silos (CFL), which offers a solution for data silos with sample misalignment without the need for sharing original or representative data to maintain privacy. CFL begins with local acquisition of contrastive representations of the data within each silo and aggregates knowledge from other silos through the federated learning algorithm. Our experiments demonstrate that CFL solves the limitations of existing algorithms for data silos and outperforms existing tabular contrastive learning. CFL provides performance improvements without loosening privacy.