Accelerating Federated Learning for IoT in Big Data Analytics With Pruning, Quantization and Selective Updating
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The ever-increasing number of Internet of Things (IoT) devices are continuously generating huge masses of data, but the current cloud-centric approach for IoT big data analysis has raised public concerns on both data privacy and network cost. Federated learning (FL) recently emerges as a promising technique to accommodate these concerns, by means of learning a global model by aggregating local updates from multiple devices without sharing the privacy-sensitive data. However, IoT devices usually have constrained computation resources and poor network connections, making it infeasible or very slow to train deep neural networks (DNNs) by following the FL pattern. To address this problem, we propose a new efficient FL framework called FL-PQSU in this paper. It is composed of 3-stage pipeline: structured pruning, weight quantization and selective updating, that work together to reduce the costs of computation, storage, and communication to accelerate the FL training process. We study FL-PQSU using popular DNN models (AlexNet, VGG16) and publicly available datasets (MNIST, CIFAR10), and demonstrate that it can well control the training overhead while still guaranteeing the learning performance.
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FL ensures data privacy and eliminates the need to transmit sensitive user data from one location to another.
“As a result, data privacy is also ensured, and there is no need to transmit sensitive user data from one location to another [245].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [245]
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PECC: Position Encoding Coordinate Classification System Design for Human Pose Estimation
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Self-citationTransformers for Internet of Things Security and Communications: A Comprehensive Survey
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The Necessity to Adopt Big Data Technologies for Efficient Performance Evaluation in the Modern Era
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Reviewing and Discussing Graph Reduction in Edge Computing Context
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NoSQL Cloud based Bigdata technologies for efficient performance evaluation in the modern era
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Decentralized Communication-assisted Sensing based on Federated Learning Framework for IIoT
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WeightRelay: Efficient Heterogeneous Federated Learning on Time Series
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Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs
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HaLo-FL: Hardware-Aware Low-Precision Federated Learning
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- Authors
- Xu, W., Fang, W., Ding, Y., Zou, M., & Xiong, N.
- Affiliations
- Beijing Jiaotong University (CN)
- Beijing Wuzi University (CN)
- Tianjin University (CN)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 38457-38466
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3133504636
- Cited by
- 112
- References
- 26
- Citations by year
- 2026: 11 · 2025: 23 · 2024: 37 · 2023: 17 · 2022: 17 · 2021: 7
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- Beijing Municipal Natural Science Foundation (L191019)
- Open Project of Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education (2019FF03)
- Open Project of Beijing Intelligent Logistics System Collaborative Innovation Center (BILSCIC-2019KF-10)
- CERNET Innovation Project (NGII20190308)
- Traffic Control Technology Innovation Fund (9907006515)
- Research Base Project of Beijing Municipal Social Science Foundation (18JDGLB026)
- Science and Technique General Program of Beijing Municipal Commission of Education (KM201910037003)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- federated learning, Internet of Things, structured pruning, weight quantization, deep neural network, resource-constrained devices, communication cost reduction, privacy preservation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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