# Private Federated Learning with Domain Adaptation

arXiv: https://arxiv.org/abs/1912.06733 · HTML: https://mlsanity.com/paper/1912.06733/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 30 of 36)

- [Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning](https://mlsanity.com/paper/2203.09249/) (2022)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2503.07137/) (2025)
- [Recent Advances on Federated Learning: A Systematic Survey](https://mlsanity.com/paper/2301.01299/) (2023)
- [FedJAX: Federated learning simulation with JAX](https://mlsanity.com/paper/2108.02117/) (2021)
- [Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability](https://mlsanity.com/paper/2111.10934/) (2021)
- [PFL-MoE: Personalized Federated Learning Based on Mixture of Experts](https://mlsanity.com/paper/2012.15589/) (2020)
- [Algorithm Fairness in AI for Medicine and Healthcare](https://mlsanity.com/paper/2110.00603/) (2021)
- [Memory-aware curriculum federated learning for breast cancer classification](https://mlsanity.com/paper/2107.02504/) (2021)
- [On Bridging Generic and Personalized Federated Learning for Image Classification](https://mlsanity.com/paper/2107.00778/) (2021)
- [Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures](https://mlsanity.com/paper/2411.19128/) (2024)
- [FedHarmony: Unlearning Scanner Bias with Distributed Data](https://mlsanity.com/paper/2205.15970/) (2022)
- [FedHB: Hierarchical Bayesian Federated Learning](https://mlsanity.com/paper/2305.04979/) (2023)
- [Practical and Secure Federated Recommendation with Personalized Masks](https://mlsanity.com/paper/2109.02464/) (2021)
- [MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes](https://mlsanity.com/paper/2404.09232/) (2024)
- [Federated Learning of Shareable Bases for Personalization-Friendly Image Classification](https://mlsanity.com/paper/2304.07882/) (2023)
- [On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates](https://mlsanity.com/paper/2106.07094/) (2021)
- [VFed-SSD: Towards Practical Vertical Federated Advertising](https://mlsanity.com/paper/2205.15987/) (2022)
- [FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations](https://mlsanity.com/paper/2302.01068/) (2023)
- [Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes](https://mlsanity.com/paper/2107.11954/) (2021)
- [Adaptive Expert Models for Personalization in Federated Learning](https://mlsanity.com/paper/2206.07832/) (2022)
- [Personalised Federated Learning: A Combinational Approach](https://mlsanity.com/paper/2108.09618/) (2021)
- [MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference](https://mlsanity.com/paper/2209.13883/) (2022)
- [Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions](https://mlsanity.com/paper/2103.07491/) (2021)
- [Preliminary Steps Towards Federated Sentiment Classification](https://mlsanity.com/paper/2107.11956/) (2021)
- [Modular Gaussian Processes for Transfer Learning](https://mlsanity.com/paper/2110.13515/) (2021)
- [Federated Semi-Supervised Domain Adaptation via Knowledge Transfer](https://mlsanity.com/paper/2207.10727/) (2022)
- [Population Expansion for Training Language Models with Private Federated Learning](https://mlsanity.com/paper/2307.07477/) (2023)
- [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024)
- [Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering](https://mlsanity.com/paper/2407.19331/) (2024)

## Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy. We propose a framework to augment this collaborative model-building with per-user domain adaptation. We show that this technique improves model accuracy for all users, using both real and synthetic data, and that this improvement is much more pronounced when differential privacy bounds are imposed on the FL model.