Data to 14 Oct 2026

Distributed learning of deep neural network over multiple agents

arXiv 1810.06060 ↗First posted 14 Oct 2018cs.LG stat.MLcited by 279

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Abstract (arXiv)

In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.

Key facts. Cited by 279 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Oct 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 35; 2022: 50; 2023: 57; 2024: 52; 2025: 53; 2026: 32.

279 papers.

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2021Towards Personalized Federated Learningcited by 464
2021A Field Guide to Federated Optimizationcited by 315
2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 138
2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 128
2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 97
2021Label Leakage and Protection in Two-party Split Learningcited by 90
2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 80
2021Decentralized Federated Learning through Proxy Model Sharingcited by 57
2021UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learningcited by 52
2021FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learningcited by 39
2022No Free Lunch Theorem for Security and Utility in Federated Learningcited by 39
2022ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learningcited by 36

38 more

2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
2022Trading Off Privacy, Utility and Efficiency in Federated Learningcited by 33
2023Optimal Resource Allocation for U-Shaped Parallel Split Learningcited by 33
2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
2023Split-and-Denoise: Protect large language model inference with local differential privacycited by 32
2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
2022Label Leakage and Protection from Forward Embedding in Vertical Federated Learningcited by 27
2022Differentially Private Label Protection in Split Learningcited by 26
2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
2021Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learningcited by 23
2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
2023GradientCoin: A Peer-to-Peer Decentralized Large Language Modelscited by 23
2023Federated Learning for Computer Visioncited by 22
2022A Survey on Over-the-Air Computationcited by 21
2023Privacy and Efficiency of Communications in Federated Split Learningcited by 21
2023PeFLL: Personalized Federated Learning by Learning to Learncited by 21
2021Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Thingscited by 20
2021SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learningcited by 20
2022SplitGP: Achieving Both Generalization and Personalization in Federated Learningcited by 20
2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
2023AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systemscited by 19
2023Communication-Efficient Split Learning via Adaptive Feature-Wise Compressioncited by 18
2025Rethinking Data Protection in the (Generative) Artificial Intelligence Eracited by 18
2021Revealing and Protecting Labels in Distributed Trainingcited by 17
2021Federated Split Vision Transformer for COVID-19 CXR Diagnosis using Task-Agnostic Trainingcited by 17
2023Accelerating Split Federated Learning over Wireless Communication Networkscited by 16
2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devicescited by 16
2021ExPLoit: Extracting Private Labels in Split Learningcited by 15
2022Feature Space Hijacking Attacks against Differentially Private Split Learningcited by 15
2022Similarity-based Label Inference Attack against Training and Inference of Split Learningcited by 15
2022SC2 Benchmark: Supervised Compression for Split Computingcited by 15
2023Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learningcited by 15
2021Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGXcited by 14
2022Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learningcited by 14
2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
2023PrivateLoRA For Efficient Privacy Preserving LLMcited by 14
2021Federated Deep Learning with Bayesian Privacycited by 13

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