mlsanityData to 7 Oct 2026

No Peek: A Survey of private distributed deep learning

arXiv 1812.03288 ↗First posted 8 Dec 2018cs.LG cs.DC stat.MLcited by 30

Not on the map. Why?

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.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Dec 2018.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 15; 2022: 7; 2023: 6; 2024: 1; 2025: 1.

30 papers.

Sort:

  1. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  2. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  3. 2021UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learningcited by 52
  4. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  5. 2021Practical Defences Against Model Inversion Attacks for Split Neural Networkscited by 29
  6. 2021Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretabilitycited by 28
  7. 2022Label Leakage and Protection from Forward Embedding in Vertical Federated Learningcited by 26
  8. 2021Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Thingscited by 20
  9. 2021Defending against Reconstruction Attack in Vertical Federated Learningcited by 20
  10. 2021SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learningcited by 20
  11. 2021FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learningcited by 14
  12. 2022Feature Space Hijacking Attacks against Differentially Private Split Learningcited by 14
18 more
  1. 2022Binarizing Split Learning for Data Privacy Enhancement and Computation Reductioncited by 11
  2. 2021Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Designcited by 9
  3. 2021Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attackscited by 8
  4. 2021AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learningcited by 8
  5. 2023On Feasibility of Server-side Backdoor Attacks on Split Learningcited by 6
  6. 2022LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Datacited by 5
  7. 2022Federated Stochastic Primal-dual Learning with Differential Privacycited by 5
  8. 2023SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detectioncited by 4
  9. 2021Syft 0.5: A Platform for Universally Deployable Structured Transparencycited by 3
  10. 2022UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenescited by 3
  11. 2023PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientscited by 3
  12. 2023SLPerf: a Unified Framework for Benchmarking Split Learningcited by 3
  13. 2021Vulnerability Due to Training Order in Split Learningcited by 2
  14. 2024Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformationcited by 2
  15. 2021Privacy-Preserving Eye-tracking Using Deep Learningcited by 0
  16. 2023Federated Split Learning with Only Positive Labels for resource-constrained IoT environmentcited by 0
  17. 2023Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environmentscited by 0
  18. 2025SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Datacited by 0