# No Peek: A Survey of private distributed deep learning

arXiv: https://arxiv.org/abs/1812.03288 · HTML: https://mlsanity.com/paper/1812.03288/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2018.

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

- [Federated Learning on Non-IID Data: A Survey](https://mlsanity.com/paper/2106.06843/) (2021)
- [No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data](https://mlsanity.com/paper/2106.05001/) (2021)
- [UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning](https://mlsanity.com/paper/2108.09033/) (2021)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [Practical Defences Against Model Inversion Attacks for Split Neural Networks](https://mlsanity.com/paper/2104.05743/) (2021)
- [Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability](https://mlsanity.com/paper/2111.10934/) (2021)
- [Label Leakage and Protection from Forward Embedding in Vertical Federated Learning](https://mlsanity.com/paper/2203.01451/) (2022)
- [Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things](https://mlsanity.com/paper/2103.02762/) (2021)
- [Defending against Reconstruction Attack in Vertical Federated Learning](https://mlsanity.com/paper/2107.09898/) (2021)
- [SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learning](https://mlsanity.com/paper/2108.09052/) (2021)
- [Feature Space Hijacking Attacks against Differentially Private Split Learning](https://mlsanity.com/paper/2201.04018/) (2022)
- [FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning](https://mlsanity.com/paper/2106.02310/) (2021)
- [Binarizing Split Learning for Data Privacy Enhancement and Computation Reduction](https://mlsanity.com/paper/2206.04864/) (2022)
- [Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design](https://mlsanity.com/paper/2105.06256/) (2021)
- [Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks](https://mlsanity.com/paper/2107.06304/) (2021)
- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [On Feasibility of Server-side Backdoor Attacks on Split Learning](https://mlsanity.com/paper/2302.09578/) (2023)
- [LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data](https://mlsanity.com/paper/2203.04305/) (2022)
- [Federated Stochastic Primal-dual Learning with Differential Privacy](https://mlsanity.com/paper/2204.12284/) (2022)
- [SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection](https://mlsanity.com/paper/2302.08618/) (2023)
- [Syft 0.5: A Platform for Universally Deployable Structured Transparency](https://mlsanity.com/paper/2104.12385/) (2021)
- [UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes](https://mlsanity.com/paper/2211.11950/) (2022)
- [PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients](https://mlsanity.com/paper/2303.10624/) (2023)
- [SLPerf: a Unified Framework for Benchmarking Split Learning](https://mlsanity.com/paper/2304.01502/) (2023)
- [Vulnerability Due to Training Order in Split Learning](https://mlsanity.com/paper/2103.14291/) (2021)
- [Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation](https://mlsanity.com/paper/2410.09125/) (2024)
- [Privacy-Preserving Eye-tracking Using Deep Learning](https://mlsanity.com/paper/2106.09621/) (2021)
- [Federated Split Learning with Only Positive Labels for resource-constrained IoT environment](https://mlsanity.com/paper/2307.13266/) (2023)
- [Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments](https://mlsanity.com/paper/2310.18479/) (2023)
- [SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2511.09828/) (2025)

## Abstract (arXiv)

We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still allowing servers to train models. The distributed deep learning methods of federated learning, split learning and large batch stochastic gradient descent are compared in addition to private and secure approaches of differential privacy, homomorphic encryption, oblivious transfer and garbled circuits in the context of neural networks. We study their benefits, limitations and trade-offs with regards to computational resources, data leakage and communication efficiency and also share our anticipated future trends.