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FLEE: A Hierarchical Federated Learning Framework for Distributed Deep Neural Network over Cloud, Edge, and End Device

https://doi.org/10.1145/3514501
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-46493-0_32openalex-snapshot
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Federated learning with adaptive communication compression under dynamic bandwidth and unreliable networks
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Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation
inferredcites →https://doi.org/10.1109/access.2020.2971524openalex-snapshot
Deep Learning Approach for Apple Edge Detection to Remotely Monitor Apple Growth in Orchards
inferredcites →https://doi.org/10.1109/cic.2018.00042openalex-snapshot
Smart Surveillance as an Edge Network Service: From Harr-Cascade, SVM to a Lightweight CNN
inferredcites →https://doi.org/10.1109/comst.2020.2970550openalex-snapshot
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00244openalex-snapshot
Resolution Adaptive Networks for Efficient Inference
inferredcites →https://doi.org/10.1109/edge.2018.00013openalex-snapshot
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inferredcites →https://doi.org/10.1109/eurospw51379.2020.00053openalex-snapshot
Enhancing Privacy via Hierarchical Federated Learning
inferredcites →https://doi.org/10.1109/hotweb.2015.22openalex-snapshot
Fog Computing: Platform and Applications
inferredcites →https://doi.org/10.1109/icassp40776.2020.9054634openalex-snapshot
Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks
inferredcites →https://doi.org/10.1109/icc40277.2020.9148862openalex-snapshot
Client-Edge-Cloud Hierarchical Federated Learning
inferredcites →https://doi.org/10.1109/iccv.2019.00144openalex-snapshot
Distillation-Based Training for Multi-Exit Architectures
inferredcites →https://doi.org/10.1109/iccv.2019.00198openalex-snapshot
Improved Techniques for Training Adaptive Deep Networks
inferredcites →https://doi.org/10.1109/icdcs.2017.226openalex-snapshot
Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices
inferredcites →https://doi.org/10.1109/icpr.2016.7900006openalex-snapshot
BranchyNet: Fast inference via early exiting from deep neural networks
inferredcites →https://doi.org/10.1109/icpr48806.2021.9412388openalex-snapshot
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks
inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9207469openalex-snapshot
Federated learning with hierarchical clustering of local updates to improve training on non-IID data
inferredcites →https://doi.org/10.1109/infocom.2019.8737464openalex-snapshot
Federated Learning over Wireless Networks: Optimization Model Design and Analysis
inferredcites →https://doi.org/10.1109/jsac.2019.2904348openalex-snapshot
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
inferredcites →https://doi.org/10.1109/jstsp.2020.2979669openalex-snapshot
Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference
inferredcites →https://doi.org/10.1109/lifetech52111.2021.9391874openalex-snapshot
A Bed-Exit and Bedside Fall Warning System Based on Deep Learning Edge Computing Techniques
inferredcites →https://doi.org/10.1109/mcom.001.2000410openalex-snapshot
From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks
inferredcites →https://doi.org/10.1109/micro.2016.7783725openalex-snapshot
Fused-layer CNN accelerators
inferredcites →https://doi.org/10.1109/tc.2021.3057082openalex-snapshot
Lime: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information Networks
inferredcites →https://doi.org/10.1109/tcad.2018.2858384openalex-snapshot
DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters
inferredcites →https://doi.org/10.1109/tii.2021.3113708openalex-snapshot
Privacy-Preserving Federated Depression Detection From Multisource Mobile Health Data
inferredcites →https://doi.org/10.1109/tnnls.2019.2944481openalex-snapshot
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3093337.3037698openalex-snapshot
Neurosurgeon
inferredcites →https://doi.org/10.1145/3274783.3275199openalex-snapshot
ECRT
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Federated Machine Learning
inferredcites →https://doi.org/10.1145/3317572openalex-snapshot
Deep Reinforcement Learning for Vehicular Edge Computing
inferredcites →https://doi.org/10.1145/3426239openalex-snapshot
Deep Learning Thermal Image Translation for Night Vision Perception
inferredcites →https://doi.org/10.1145/3459637.3482252openalex-snapshot
Differentially Private Federated Knowledge Graphs Embedding
inferredcites →https://doi.org/10.1145/3490181openalex-snapshot
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
inferredcites →https://doi.org/10.1609/aaai.v33i01.33011190openalex-snapshot
Private Model Compression via Knowledge Distillation
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.504openalex-snapshot
The Cascade Transformer: an Application for Efficient Answer Sentence Selection
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.537openalex-snapshot
FastBERT: a Self-distilling BERT with Adaptive Inference Time
inferredcites →https://doi.org/10.18653/v1/2021.naacl-main.162openalex-snapshot
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models
inferredcites →https://doi.org/10.19210/1002.2.1.16openalex-snapshot
Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing
inferredcites →https://doi.org/10.23919/jcc.2020.02.010openalex-snapshot
Face image recognition based on convolutional neural network
inferredcites →https://doi.org/10.3115/v1/d14-1181openalex-snapshot
Convolutional Neural Networks for Sentence Classification

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Heterogeneous Communication in Decentralized Federated Learning
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Federated fusion learning with attention mechanism for multi-client medical image analysis
inferred← citeshttps://doi.org/10.1016/j.iot.2026.102055openalex-snapshot
LHFL: Logical hierarchical federated learning mechanism in distributed physical AI networks
inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126327openalex-snapshot
Deep neural networks in the cloud: Review, applications, challenges and research directions
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.132293openalex-snapshot
Neighbor-enhanced federated graph learning with feature knowledge distillation
inferred← citeshttps://doi.org/10.1016/j.sysarc.2023.103052openalex-snapshot
Hydra: Hybrid-model federated learning for human activity recognition on heterogeneous devices
inferred← citeshttps://doi.org/10.1038/s41598-025-16830-8openalex-snapshot
RAIM: three-stage stackelberg game for hierarchical federated learning with reputation-aware incentive mechanism
inferred← citeshttps://doi.org/10.1109/apcit65661.2025.11411518openalex-snapshot
ConvNexFed: A Federated Learning Framework for Image Classification with CNN
inferred← citeshttps://doi.org/10.1109/comst.2025.3568279openalex-snapshot
Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
inferred← citeshttps://doi.org/10.1109/cvpr52734.2025.01441openalex-snapshot
FedSPA : Generalizable Federated Graph Learning under Homophily Heterogeneity
inferred← citeshttps://doi.org/10.1109/cyberscitech68397.2025.00019openalex-snapshot
XAI-based Customer Behavior Analysis for Network Intrusion Detection in Edge Intelligence
inferred← citeshttps://doi.org/10.1109/dcoss-iot69657.2026.00089openalex-snapshot
Efficient Hierarchical Federated Learning with Dynamic Cluster Formation for IoT Wearable Analytics
inferred← citeshttps://doi.org/10.1109/fmec65595.2025.11119244openalex-snapshot
Hierarchical Bidirectional Aggregation for Federated Learning Under Concept Drift
inferred← citeshttps://doi.org/10.1109/iecon55916.2024.10905560openalex-snapshot
A Cloud-Edge Intelligent Collaborative Framework and Its Applications in AIGC and Digital Twins
inferred← citeshttps://doi.org/10.1109/iotm.001.2300229openalex-snapshot
Toward a Scalable and Energy-Efficient Framework for Industrial Cloud-Edge-IoT Continuum
inferred← citeshttps://doi.org/10.1109/jiot.2023.3290818openalex-snapshot
QuAsyncFL: Asynchronous Federated Learning With Quantization for Cloud–Edge–Terminal Collaboration Enabled AIoT
inferred← citeshttps://doi.org/10.1109/jiot.2023.3314743openalex-snapshot
Low-Latency Hierarchical Federated Learning in Wireless Edge Networks
inferred← citeshttps://doi.org/10.1109/tnnls.2025.3565827openalex-snapshot
SacFL: Self-Adaptive Federated Continual Learning for Resource-Constrained End Devices
inferred← citeshttps://doi.org/10.1109/tpami.2026.3711828openalex-snapshot
Robust Personalized Federated Continual Learning via Explainable Multi-Granularity Prompt
inferred← citeshttps://doi.org/10.1109/tpds.2025.3611304openalex-snapshot
FedSR: A Semi-Decentralized Federated Learning Framework for Non-IID Data Based on Incremental Subgradient Optimization
inferred← citeshttps://doi.org/10.1145/3664650openalex-snapshot
Advancements in Federated Learning: Models, Methods, and Privacy
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Efficient federated learning via aggregation of base models
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Deep Learning Neural Networks in the Cloud
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Federated Learning for Secure and Resilient AI Systems
inferred← citeshttps://doi.org/10.5753/courb.2023.717openalex-snapshot
ATHENA-FL: Evitando a Heterogeneidade Estatística através do Um-contra-Todos no Aprendizado Federado
inferred← citeshttps://doi.org/10.5753/jisa.2024.3826openalex-snapshot
ATHENA-FL: Avoiding Statistical Heterogeneity with One-versus-All in Federated Learning
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Federated Learning with Accurate Model Training and Low Communication Cost in Heterogeneous Scenarios
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