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Efficient asynchronous federated learning with sparsification and quantization

https://doi.org/10.1002/cpe.8002
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What this work says

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

inferredcites →https://doi.org/10.1002/cpe.7697openalex-snapshot
Distributed and deep vertical federated learning with big data
inferredcites →https://doi.org/10.1007/s10115-022-01664-xopenalex-snapshot
From distributed machine learning to federated learning: a survey
inferredcites →https://doi.org/10.1007/s10489-023-05239-3openalex-snapshot
On mask-based image set desensitization with recognition support
inferredcites →https://doi.org/10.1016/j.cosrev.2023.100595openalex-snapshot
Asynchronous federated learning on heterogeneous devices: A survey
inferredcites →https://doi.org/10.1016/j.future.2023.05.032openalex-snapshot
HeterPS: Distributed deep learning with reinforcement learning based scheduling in heterogeneous environments
inferredcites →https://doi.org/10.1109/bigdata50022.2020.9378161openalex-snapshot
Asynchronous Online Federated Learning for Edge Devices with Non-IID Data
inferredcites →https://doi.org/10.1109/icc.2019.8761315openalex-snapshot
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
inferredcites →https://doi.org/10.1109/icdcs51616.2021.00060openalex-snapshot
GRACE: A Compressed Communication Framework for Distributed Machine Learning
inferredcites →https://doi.org/10.1109/icdm54844.2022.00013openalex-snapshot
Federated Fingerprint Learning with Heterogeneous Architectures
inferredcites →https://doi.org/10.1109/infocom52122.2024openalex-snapshot
IEEE INFOCOM 2024 - IEEE Conference on Computer Communications
inferredcites →https://doi.org/10.1109/infocom53939.2023.10229075openalex-snapshot
Asynchronous Federated Unlearning
inferredcites →https://doi.org/10.1109/iwqos54832.2022.9812885openalex-snapshot
How Asynchronous can Federated Learning Be?
inferredcites →https://doi.org/10.1109/jiot.2022.3181136openalex-snapshot
Privacy-Aware Point-of-Interest Category Recommendation in Internet of Things
inferredcites →https://doi.org/10.1109/jiot.2023.3299573openalex-snapshot
Client Selection in Federated Learning: Principles, Challenges, and Opportunities
inferredcites →https://doi.org/10.1109/jproc.2019.2921977openalex-snapshot
Deep Learning With Edge Computing: A Review
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/mnet.2019.1800286openalex-snapshot
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning
inferredcites →https://doi.org/10.1109/tc.2020.2994391openalex-snapshot
SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead
inferredcites →https://doi.org/10.1109/tii.2019.2909473openalex-snapshot
Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing
inferredcites →https://doi.org/10.1109/tmc.2022.3186936openalex-snapshot
Adaptive Control of Local Updating and Model Compression for Efficient Federated Learning
inferredcites →https://doi.org/10.1109/tnnls.2019.2953131openalex-snapshot
Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation
inferredcites →https://doi.org/10.1109/tpds.2022.3224941openalex-snapshot
Multi-Job Intelligent Scheduling With Cross-Device Federated Learning
inferredcites →https://doi.org/10.1109/twc.2020.3025446openalex-snapshot
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
inferredcites →https://doi.org/10.1145/3298981openalex-snapshot
Federated Machine Learning
inferredcites →https://doi.org/10.1145/3457388.3458655openalex-snapshot
TEA-fed
inferredcites →https://doi.org/10.1145/3495243.3517017openalex-snapshot
PyramidFL
inferredcites →https://doi.org/10.1145/3545008.3545065openalex-snapshot
FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity
inferredcites →https://doi.org/10.1145/3620677openalex-snapshot
Privacy-preserving Point-of-interest Recommendation based on Simplified Graph Convolutional Network for Geological Traveling
inferredcites →https://doi.org/10.1162/neco_a_00990openalex-snapshot
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
inferredcites →https://doi.org/10.18653/v1/2023.emnlp-main.488openalex-snapshot
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
inferredcites →https://doi.org/10.18653/v1/d17-1045openalex-snapshot
Sparse Communication for Distributed Gradient Descent
inferredcites →https://doi.org/10.24963/ijcai.2022/385openalex-snapshot
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-981-96-2864-3_13openalex-snapshot
Federal Knowledge Graph Embedding Based on Incentive Mechanism
inferred← citeshttps://doi.org/10.1007/s10115-024-02117-3openalex-snapshot
Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning
inferred← citeshttps://doi.org/10.1007/s10115-024-02285-2openalex-snapshot
Trustworthy federated learning: privacy, security, and beyond
inferred← citeshttps://doi.org/10.1007/s10115-025-02546-8openalex-snapshot
Efficient federated learning with timely update dissemination
inferred← citeshttps://doi.org/10.1016/j.datak.2025.102422openalex-snapshot
Privacy-preserving cross-network service recommendation via federated learning of unified user representations
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114246openalex-snapshot
Communication-efficient personalized federated learning with evolutionary strategies and cluster-aware knowledge transfer
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.133142openalex-snapshot
Towards robust neurocomputing model in efficient federated brain tumour segmentation with sparsification and weights clustering
inferred← citeshttps://doi.org/10.1016/j.sasc.2026.200605openalex-snapshot
Noise-coordinated federated reinforcement learning for campus edge Internet of Things
inferred← citeshttps://doi.org/10.1016/j.sysarc.2025.103523openalex-snapshot
A verifiable efficient federated learning method based on adaptive Boltzmann selection for data processing in the internet of things
inferred← citeshttps://doi.org/10.1021/acs.iecr.4c03805openalex-snapshot
Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration across Distributed Data Sources
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/icdsca67083.2025.11349885openalex-snapshot
Cooperative Optimization of FedBuff and Gradient Sparsification: A Study for Low-Bandwidth Federated Learning Scenarios
inferred← citeshttps://doi.org/10.1109/tmc.2026.3683745openalex-snapshot
CSAFL: Communication-Efficient and Staleness-Aware Asynchronous Federated Learning for LEO Satellite Networks
inferred← citeshttps://doi.org/10.1109/tmc.2026.3688489openalex-snapshot
AsyncFed-SAR: Toward Communication-Efficient Asynchronous Federated Learning for SAR Image Target Recognition
inferred← citeshttps://doi.org/10.1145/3674213.3674215openalex-snapshot
On the Impact of Heterogeneity on Federated Learning at the Edge with DGA Malware Detection
inferred← citeshttps://doi.org/10.1145/3690648openalex-snapshot
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
inferred← citeshttps://doi.org/10.1145/3749376openalex-snapshot
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
inferred← citeshttps://doi.org/10.1177/10692509261490475openalex-snapshot
An efficient federated learning method based on the proportional-integral-derivative mechanism for embedded devices
inferred← citeshttps://doi.org/10.1609/aaai.v38i12.29297openalex-snapshot
FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update
inferred← citeshttps://doi.org/10.3390/iot6030035openalex-snapshot
Data-Bound Adaptive Federated Learning: FedAdaDB
inferred← citeshttps://doi.org/10.5753/jisa.2024.4006openalex-snapshot
The Impact of Federated Learning on Urban Computing
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