# An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies

arXiv: https://arxiv.org/abs/2004.04676 · HTML: https://mlsanity.com/paper/2004.04676/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

## Built on by (most cited first, 18 of 18)

- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://mlsanity.com/paper/2111.11066/) (2021)
- [FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations](https://mlsanity.com/paper/2203.13789/) (2022)
- [FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation](https://mlsanity.com/paper/2205.14900/) (2022)
- [Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction](https://mlsanity.com/paper/2311.05808/) (2023)
- [FedLess: Secure and Scalable Federated Learning Using Serverless Computing](https://mlsanity.com/paper/2111.03396/) (2021)
- [Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data](https://mlsanity.com/paper/2205.03168/) (2022)
- [Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning](https://mlsanity.com/paper/2101.00159/) (2021)
- [A Generalist Model for Diverse Text-Guided Medical Image Synthesis](https://mlsanity.com/paper/2405.09806/) (2024)
- [PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning](https://mlsanity.com/paper/2208.05320/) (2022)
- [NLICE: Synthetic Medical Record Generation for Effective Primary Healthcare Differential Diagnosis](https://mlsanity.com/paper/2401.13756/) (2024)
- [Vanishing Variance Problem in Fully Decentralized Neural-Network Systems](https://mlsanity.com/paper/2404.04616/) (2024)
- [From Risk to Resilience: Towards Assessing and Mitigating the Risk of Data Reconstruction Attacks in Federated Learning](https://mlsanity.com/paper/2512.15460/) (2025)
- [Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy](https://mlsanity.com/paper/2405.10376/) (2024)
- [Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning](https://mlsanity.com/paper/2402.07295/) (2024)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)
- [Anomalous Client Detection in Federated Learning](https://mlsanity.com/paper/2411.01490/) (2024)
- [TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems](https://mlsanity.com/paper/2506.07605/) (2025)

## Abstract (arXiv)

With the increased attention and legislation for data-privacy, collaborative machine learning (ML) algorithms are being developed to ensure the protection of private data used for processing. Federated learning (FL) is the most popular of these methods, which provides privacy preservation by facilitating collaborative training of a shared model without the need to exchange any private data with a centralized server. Rather, an abstraction of the data in the form of a machine learning model update is sent. Recent studies showed that such model updates may still very well leak private information and thus more structured risk assessment is needed. In this paper, we analyze existing vulnerabilities of FL and subsequently perform a literature review of the possible attack methods targetingFL privacy protection capabilities. These attack methods are then categorized by a basic taxonomy. Additionally, we provide a literature study of the most recent defensive strategies and algorithms for FL aimed to overcome these attacks. These defensive strategies are categorized by their respective underlying defence principle. The paper concludes that the application of a single defensive strategy is not enough to provide adequate protection to all available attack methods.