A generic framework for privacy preserving deep learning
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Abstract (arXiv)
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user. We report early results on the Boston Housing and Pima Indian Diabetes datasets. While the privacy features apart from Differential Privacy do not impact the prediction accuracy, the current implementation of the framework introduces a significant overhead in performance, which will be addressed at a later stage of the development. We believe this work is an important milestone introducing the first reliable, general framework for privacy preserving deep learning.
Key facts. Cited by 103 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Nov 2018.
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Citing papers per year: 2021: 47; 2022: 24; 2023: 18; 2024: 6; 2025: 3; 2026: 5.
103 papers.
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- 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
- 2021A Field Guide to Federated Optimizationcited by 312
- 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
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- 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
- 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
- 2021FedJAX: Federated learning simulation with JAXcited by 32
- 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
38 more
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- 2023Privacy and Efficiency of Communications in Federated Split Learningcited by 20
- 2021Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detectioncited by 19
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- 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
- 2021Secure Quantized Training for Deep Learningcited by 15
- 2021FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learningcited by 15
- 2022Secure Aggregation for Federated Learning in Flowercited by 15
- 2021Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunitiescited by 14
- 2021Privacy and Trust Redefined in Federated Machine Learningcited by 13
- 2021On-device Federated Learning with Flowercited by 13
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- 2021A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learningcited by 12
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- 2021Self-supervised Cross-silo Federated Neural Architecture Searchcited by 11
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- 2022BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learningcited by 7
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- 2023Flame: Simplifying Topology Extension in Federated Learningcited by 7
- 2024COALA: A Practical and Vision-Centric Federated Learning Platformcited by 7
- 2021Enabling Homomorphically Encrypted Inference for Large DNN Modelscited by 6
- 2021Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliabilitycited by 6
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50 most cited of 103 All 103 (JSON)
- 2026Homomorphic Encryptions for Privacy Preserving Vision23 Jun 2026
- 2026Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences22 Jun 2026
- 2026The Nonverbal Gap: Toward Affective Computer Vision for Safer and More Equitable Online Dating20 Mar 2026
- 2026LEA: Label Enumeration Attack in Vertical Federated Learning4 Mar 2026
- 2026Secure, Verifiable, and Scalable Multi-Client Data Sharing via Consensus-Based Privacy-Preserving Data Distribution1 Jan 2026
- 2025AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing9 Jul 2025
- 2025ByzFL: Research Framework for Robust Federated Learning30 May 2025
- 2025A Federated Random Forest Solution for Secure Distributed Machine Learning12 May 2025
- 2024Assistive AI for Augmenting Human Decision-making18 Oct 2024
- 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare15 Sep 2024
- 2024FedModule: A Modular Federated Learning Framework7 Sep 2024
- 2024A Web-Based Solution for Federated Learning with LLM-Based Automation23 Aug 2024
38 more
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- 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights7 Dec 2023
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- 2023VFLAIR: A Research Library and Benchmark for Vertical Federated Learning15 Oct 2023
- 2023FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks3 Oct 2023
- 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning1 Sep 2023
- 2023A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks9 Aug 2023
- 2023FLGo: A Fully Customizable Federated Learning Platform21 Jun 2023
- 2023Flame: Simplifying Topology Extension in Federated Learning9 May 2023
- 2023MLHOps: Machine Learning for Healthcare Operations4 May 2023
- 2023Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications24 Apr 2023
- 2023SLPerf: a Unified Framework for Benchmarking Split Learning4 Apr 2023
- 2023Exploring the Relevance of Data Privacy-Enhancing Technologies for AI Governance Use Cases15 Mar 2023
- 2023FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training3 Mar 2023
- 2023Towards Automated Homomorphic Encryption Parameter Selection with Fuzzy Logic and Linear Programming17 Feb 2023
- 2023XFL: A High Performace, Lightweighted Federated Learning Framework10 Feb 2023
- 2023Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach1 Feb 2023
- 2023Privacy and Efficiency of Communications in Federated Split Learning4 Jan 2023
- 2022Modeling Global Distribution for Federated Learning with Label Distribution Skew17 Dec 2022
- 2022Privacy-Preserving Collaborative Learning through Feature Extraction13 Dec 2022
- 2022Private Multiparty Perception for Navigation2 Dec 2022
- 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges15 Nov 2022
- 2022TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments1 Nov 2022
- 2022Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models24 Oct 2022
- 2022Asynchronous Training Schemes in Distributed Learning with Time Delay28 Aug 2022
- 2022Efficient ML Models for Practical Secure Inference26 Aug 2022
- 2022Towards Energy-Aware Federated Learning on Battery-Powered Clients9 Aug 2022
- 2022Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning24 Jul 2022
- 2022Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond19 Jul 2022
- 2022Swan: A Neural Engine for Efficient DNN Training on Smartphone SoCs9 Jun 2022
- 2022Federated singular value decomposition for high dimensional data24 May 2022
- 2022Test-Time Robust Personalization for Federated Learning22 May 2022
- 2022Secure Aggregation for Federated Learning in Flower12 May 2022
- 2022A review of Federated Learning in Intrusion Detection Systems for IoT26 Apr 2022
- 2022Federated Learning: Balancing the Thin Line Between Data Intelligence and Privacy22 Apr 2022
- 2022Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data2 Mar 2022
50 newest of 103 All 103 (JSON)
- 2021Robust Federated Learning by Mixture of Expertscited by 4
- 2021Robust Federated Learning by Mixture of Experts23 Apr 2021
- 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
- 2021A Field Guide to Federated Optimizationcited by 312
- 2023FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learningcited by 146
- 2021CrypTen: Secure Multi-Party Computation Meets Machine Learningcited by 131
- 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 84
- 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 65
- 2021FedLab: A Flexible Federated Learning Frameworkcited by 54
- 2022Test-Time Robust Personalization for Federated Learningcited by 39
- 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
- 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
- 2021FedJAX: Federated learning simulation with JAXcited by 32
- 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
12 most cited of 102 All 102 (JSON)
- 2026Homomorphic Encryptions for Privacy Preserving Vision23 Jun 2026
- 2026Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences22 Jun 2026
- 2026The Nonverbal Gap: Toward Affective Computer Vision for Safer and More Equitable Online Dating20 Mar 2026
- 2026LEA: Label Enumeration Attack in Vertical Federated Learning4 Mar 2026
- 2026Secure, Verifiable, and Scalable Multi-Client Data Sharing via Consensus-Based Privacy-Preserving Data Distribution1 Jan 2026
- 2025AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing9 Jul 2025
- 2025ByzFL: Research Framework for Robust Federated Learning30 May 2025
- 2025A Federated Random Forest Solution for Secure Distributed Machine Learning12 May 2025
- 2024Assistive AI for Augmenting Human Decision-making18 Oct 2024
- 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare15 Sep 2024
- 2024FedModule: A Modular Federated Learning Framework7 Sep 2024
- 2024A Web-Based Solution for Federated Learning with LLM-Based Automation23 Aug 2024
12 newest of 102 All 102 (JSON)