# A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments

arXiv: https://arxiv.org/abs/2502.16065 · HTML: https://mlsanity.com/paper/2502.16065/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 60 references that are in the map.
- First posted on arXiv 22 Feb 2025.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [iDLG: Improved Deep Leakage from Gradients](https://mlsanity.com/paper/2001.02610/) (2020)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [High Accuracy and High Fidelity Extraction of Neural Networks](https://mlsanity.com/paper/1909.01838/) (2019)
- [Privacy and Robustness in Federated Learning: Attacks and Defenses](https://mlsanity.com/paper/2012.06337/) (2020)
- [PRADA: Protecting against DNN Model Stealing Attacks](https://mlsanity.com/paper/1805.02628/) (2018)
- [A Survey of Privacy Attacks in Machine Learning](https://mlsanity.com/paper/2007.07646/) (2020)
- [Stealing Hyperparameters in Machine Learning](https://mlsanity.com/paper/1802.05351/) (2018)
- [Defending Against Model Stealing Attacks with Adaptive Misinformation](https://mlsanity.com/paper/1911.07100/) (2019)
- [DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories](https://mlsanity.com/paper/2111.04625/) (2021)
- [Model Extraction Warning in MLaaS Paradigm](https://mlsanity.com/paper/1711.07221/) (2017)
- [Open DNN Box by Power Side-Channel Attack](https://mlsanity.com/paper/1907.10406/) (2019)
- [SNIFF: Reverse Engineering of Neural Networks with Fault Attacks](https://mlsanity.com/paper/2002.11021/) (2020)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints](https://mlsanity.com/paper/1903.03916/) (2019)

## Built on by (most cited first, 6 of 6)

- [CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition](https://mlsanity.com/paper/2506.17709/) (2025)
- [MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models](https://mlsanity.com/paper/2506.02362/) (2025)
- [GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It?](https://mlsanity.com/paper/2605.12827/) (2026)
- [Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses](https://mlsanity.com/paper/2508.19641/) (2025)
- [FuSeFL: Fully Secure and Scalable Federated Learning](https://mlsanity.com/paper/2507.13591/) (2025)
- [DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning](https://mlsanity.com/paper/2507.05649/) (2025)

## Abstract (arXiv)

Model Extraction Attacks (MEAs) threaten modern machine learning systems by enabling adversaries to steal models, exposing intellectual property and training data. With the increasing deployment of machine learning models in distributed computing environments, including cloud, edge, and federated learning settings, each paradigm introduces distinct vulnerabilities and challenges. Without a unified perspective on MEAs across these distributed environments, organizations risk fragmented defenses, inadequate risk assessments, and substantial economic and privacy losses. This survey is motivated by the urgent need to understand how the unique characteristics of cloud, edge, and federated deployments shape attack vectors and defense requirements. We systematically examine the evolution of attack methodologies and defense mechanisms across these environments, demonstrating how environmental factors influence security strategies in critical sectors such as autonomous vehicles, healthcare, and financial services. By synthesizing recent advances in MEAs research and discussing the limitations of current evaluation practices, this survey provides essential insights for developing robust and adaptive defense strategies. Our comprehensive approach highlights the importance of integrating protective measures across the entire distributed computing landscape to ensure the secure deployment of machine learning models.