# Privacy Assessment of Federated Learning using Private Personalized Layers

arXiv: https://arxiv.org/abs/2106.08060 · HTML: https://mlsanity.com/paper/2106.08060/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Salvaging Federated Learning by Local Adaptation](https://mlsanity.com/paper/2002.04758/) (2020)
- [Mobile Sensor Data Anonymization](https://mlsanity.com/paper/1810.11546/) (2018)
- [The Trade-Offs of Private Prediction](https://mlsanity.com/paper/2007.05089/) (2020)
- [Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting](https://mlsanity.com/paper/1709.01604/) (2017)
- [signSGD with Majority Vote is Communication Efficient And Fault Tolerant](https://mlsanity.com/paper/1810.05291/) (2018)
- [DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks](https://mlsanity.com/paper/2003.10325/) (2020)

## Built on by (most cited first, 5 of 5)

- [Cerberus: Exploring Federated Prediction of Security Events](https://mlsanity.com/paper/2209.03050/) (2022)
- [A review of federated learning in renewable energy applications: Potential, challenges, and future directions](https://mlsanity.com/paper/2312.11220/) (2023)
- [SoK: Memorisation in machine learning](https://mlsanity.com/paper/2311.03075/) (2023)
- [Leverage Unlearning to Sanitize LLMs](https://mlsanity.com/paper/2510.21322/) (2025)
- [Privacy-Preserving Action Recognition: Taxonomy, Methods, and Privacy-Utility Trade-offs](https://mlsanity.com/paper/2608.04501/) (2026)

## Abstract (arXiv)

Federated Learning (FL) is a collaborative scheme to train a learning model across multiple participants without sharing data. While FL is a clear step forward towards enforcing users' privacy, different inference attacks have been developed. In this paper, we quantify the utility and privacy trade-off of a FL scheme using private personalized layers. While this scheme has been proposed as local adaptation to improve the accuracy of the model through local personalization, it has also the advantage to minimize the information about the model exchanged with the server. However, the privacy of such a scheme has never been quantified. Our evaluations using motion sensor dataset show that personalized layers speed up the convergence of the model and slightly improve the accuracy for all users compared to a standard FL scheme while better preventing both attribute and membership inferences compared to a FL scheme using local differential privacy.