# Federated Transfer Learning with Differential Privacy

arXiv: https://arxiv.org/abs/2403.11343 · HTML: https://mlsanity.com/paper/2403.11343/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2024.

## Built on by (most cited first, 9 of 9)

- [Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests](https://mlsanity.com/paper/2406.06749/) (2024)
- [Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints](https://mlsanity.com/paper/2406.20088/) (2024)
- [Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference](https://mlsanity.com/paper/2404.16287/) (2024)
- [High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference](https://mlsanity.com/paper/2508.05212/) (2025)
- [Federated fairness-aware classification under differential privacy](https://mlsanity.com/paper/2603.24392/) (2026)
- [A Van Trees Lower Bound for Fully Interactive Differentially Private Federated Learning](https://mlsanity.com/paper/2605.19813/) (2026)
- [Harnessing Source Heterogeneity for Cluster-Structured Transfer Learning](https://mlsanity.com/paper/2606.05258/) (2026)
- [Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms](https://mlsanity.com/paper/2607.02681/) (2026)
- [High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning](https://mlsanity.com/paper/2610.02578/) (2026)

## Abstract (arXiv)

Federated learning has emerged as a powerful framework for analysing distributed data, yet two challenges remain pivotal: heterogeneity across sites and privacy of local data. In this paper, we address both challenges within a federated transfer learning framework, aiming to enhance learning on a target data set by leveraging information from multiple heterogeneous source data sets while adhering to privacy constraints. We rigorously formulate the notion of federated differential privacy, which offers privacy guarantees for each data set without assuming a trusted central server. Under this privacy model, we study four statistical problems: univariate mean estimation, low-dimensional linear regression, high-dimensional linear regression, and M-estimation. By investigating the minimax rates and quantifying the cost of privacy, we show that federated differential privacy is an intermediate privacy model between the well-established local and central models of differential privacy. Our analyses account for data heterogeneity and privacy, highlighting the fundamental costs associated with each factor and the benefits of knowledge transfer in federated learning.