FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312441490> ?p ?o ?g. }

• W4312441490 endingPage "2507" @default.
• W4312441490 startingPage "2494" @default.
• W4312441490 abstract "Federated learning (FL) allows participants to train deep learning models collaboratively without disclosing their data to the server or any other participants, providing excellent value in the field of privacy-sensitive IoT. However, this distributed training paradigm requires clients to perform intensive computation for many iterations, which may exceed the capability of a typical IoT terminal with limited processing power, storage capacity, and energy budget. Heavy communication between the server and clients may also result in intolerant bandwidth requirements and energy consumption for many IoT systems. In this article, we introduce the FedQNN, a computation–communication-efficient FL framework for IoT scenarios. It is the first work that integrates ultralow-bitwidth quantization into the FL environment, allowing clients to perform lightweight fix-point computation efficiently with less power. Furthermore, both upstream and downstream data are significantly compressed for more efficient communication using a combination of sparsification and quantization strategies. We performed extensive experiments on a variety of data sets and models while comparing with other frameworks, and the results demonstrate that the proposed method can save up to 90% of our clients’ computational energy, reduce model sizes by 30+ times, and significantly compress both communication bandwidth and transmitted data size while maintaining reasonable accuracy. The robustness against the non-independent and identically distributed (I.I.D.) condition is also validated." @default.
• W4312441490 created "2023-01-04" @default.
• W4312441490 creator A5000159988 @default.
• W4312441490 creator A5002692584 @default.
• W4312441490 date "2023-02-01" @default.
• W4312441490 modified "2023-10-02" @default.
• W4312441490 title "FedQNN: A Computation–Communication-Efficient Federated Learning Framework for IoT With Low-Bitwidth Neural Network Quantization" @default.
• W4312441490 cites W114517082 @default.
• W4312441490 cites W2078831169 @default.
• W4312441490 cites W2115613939 @default.
• W4312441490 cites W2194775991 @default.
• W4312441490 cites W2285660444 @default.
• W4312441490 cites W2300242332 @default.
• W4312441490 cites W2405578611 @default.
• W4312441490 cites W2431931973 @default.
• W4312441490 cites W2594492285 @default.
• W4312441490 cites W2730601341 @default.
• W4312441490 cites W2740067745 @default.
• W4312441490 cites W2745228312 @default.
• W4312441490 cites W2781293805 @default.
• W4312441490 cites W2786070938 @default.
• W4312441490 cites W2794141774 @default.
• W4312441490 cites W2845210056 @default.
• W4312441490 cites W2884150179 @default.
• W4312441490 cites W2906663849 @default.
• W4312441490 cites W2950025781 @default.
• W4312441490 cites W2963029056 @default.
• W4312441490 cites W2963037989 @default.
• W4312441490 cites W2963209930 @default.
• W4312441490 cites W2963480671 @default.
• W4312441490 cites W2963576971 @default.
• W4312441490 cites W2964242827 @default.
• W4312441490 cites W2989289980 @default.
• W4312441490 cites W3012561096 @default.
• W4312441490 cites W3033511014 @default.
• W4312441490 cites W3099185500 @default.
• W4312441490 cites W3101036738 @default.
• W4312441490 cites W3134750800 @default.
• W4312441490 doi "https://doi.org/10.1109/jiot.2022.3213650" @default.
• W4312441490 hasPublicationYear "2023" @default.
• W4312441490 type Work @default.
• W4312441490 citedByCount "1" @default.
• W4312441490 countsByYear W43124414902023 @default.
• W4312441490 crossrefType "journal-article" @default.
• W4312441490 hasAuthorship W4312441490A5000159988 @default.
• W4312441490 hasAuthorship W4312441490A5002692584 @default.
• W4312441490 hasConcept C104317684 @default.
• W4312441490 hasConcept C113775141 @default.
• W4312441490 hasConcept C11413529 @default.
• W4312441490 hasConcept C119599485 @default.
• W4312441490 hasConcept C119857082 @default.
• W4312441490 hasConcept C120314980 @default.
• W4312441490 hasConcept C127413603 @default.
• W4312441490 hasConcept C154945302 @default.
• W4312441490 hasConcept C185592680 @default.
• W4312441490 hasConcept C18903297 @default.
• W4312441490 hasConcept C199833920 @default.
• W4312441490 hasConcept C2742236 @default.
• W4312441490 hasConcept C2780165032 @default.
• W4312441490 hasConcept C28855332 @default.
• W4312441490 hasConcept C31258907 @default.
• W4312441490 hasConcept C41008148 @default.
• W4312441490 hasConcept C45374587 @default.
• W4312441490 hasConcept C50644808 @default.
• W4312441490 hasConcept C55493867 @default.
• W4312441490 hasConcept C63479239 @default.
• W4312441490 hasConcept C86803240 @default.
• W4312441490 hasConcept C93996380 @default.
• W4312441490 hasConceptScore W4312441490C104317684 @default.
• W4312441490 hasConceptScore W4312441490C113775141 @default.
• W4312441490 hasConceptScore W4312441490C11413529 @default.
• W4312441490 hasConceptScore W4312441490C119599485 @default.
• W4312441490 hasConceptScore W4312441490C119857082 @default.
• W4312441490 hasConceptScore W4312441490C120314980 @default.
• W4312441490 hasConceptScore W4312441490C127413603 @default.
• W4312441490 hasConceptScore W4312441490C154945302 @default.
• W4312441490 hasConceptScore W4312441490C185592680 @default.
• W4312441490 hasConceptScore W4312441490C18903297 @default.
• W4312441490 hasConceptScore W4312441490C199833920 @default.
• W4312441490 hasConceptScore W4312441490C2742236 @default.
• W4312441490 hasConceptScore W4312441490C2780165032 @default.
• W4312441490 hasConceptScore W4312441490C28855332 @default.
• W4312441490 hasConceptScore W4312441490C31258907 @default.
• W4312441490 hasConceptScore W4312441490C41008148 @default.
• W4312441490 hasConceptScore W4312441490C45374587 @default.
• W4312441490 hasConceptScore W4312441490C50644808 @default.
• W4312441490 hasConceptScore W4312441490C55493867 @default.
• W4312441490 hasConceptScore W4312441490C63479239 @default.
• W4312441490 hasConceptScore W4312441490C86803240 @default.
• W4312441490 hasConceptScore W4312441490C93996380 @default.
• W4312441490 hasFunder F4320321133 @default.
• W4312441490 hasIssue "3" @default.
• W4312441490 hasLocation W43124414901 @default.
• W4312441490 hasOpenAccess W4312441490 @default.
• W4312441490 hasPrimaryLocation W43124414901 @default.
• W4312441490 hasRelatedWork W1967289688 @default.
• W4312441490 hasRelatedWork W1973815670 @default.
• W4312441490 hasRelatedWork W2100799217 @default.

• next