Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence | Paper Citation Record

Observation 49e57b60-0fdf-4389-81d4-e0b998400b65 · outbound

This paper cites Toward a heterogeneous mist, fog, and cloud-based framework for the internet of healthcare things,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Toward a heterogeneous mist, fog, and cloud-based framework for the internet of healthcare things,

Reference 1

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Observation aa92cd70-411d-4167-93c3-eaa456baa880 · outbound

This paper cites Iot connections outlook: Nb-iot and cat-m technologies will account for close to 45 percent of cellular iot connections in 2024.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Iot connections outlook: Nb-iot and cat-m technologies will account for close to 45 percent of cellular iot connections in 2024

Reference 2

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Observation d59554f2-5914-4255-bba0-fe6d2eb3d260 · outbound

This paper cites Mobile edge computing: A survey,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Mobile edge computing: A survey,

Reference 3

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Observation 51d935d1-8cc3-4c98-a350-a5c7126b9067 · outbound

This paper cites Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Reference 4

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Observation c89e1cef-1573-4a23-a617-00458f81868f · outbound

This paper cites Deep learning for intelligent wireless networks: A comprehensive survey,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Deep learning for intelligent wireless networks: A comprehensive survey,

Reference 5

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Observation ebd6df38-a21f-4b11-bd49-58ca143f2112 · outbound

This paper cites Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Reference 6

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Observation 9371b269-8207-4df4-9908-8cf84767c8cc · outbound

This paper cites Edge intelligence: Paving the last mile of artiﬁcial intelligence with edge computing,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Edge intelligence: Paving the last mile of artiﬁcial intelligence with edge computing,

Reference 7

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Observation f94b49bd-d7ae-451c-a14a-5a6bbc5b4af6 · outbound

This paper cites Convergence of Edge Computing and Deep Learning: A Comprehensive Survey.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Reference 9

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Observation 00315b0e-e5f1-46d9-9deb-ab4b75f58e8b · outbound

This paper cites OpenEI: An Open Framework for Edge Intelligence.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence OpenEI: An Open Framework for Edge Intelligence

Reference 10

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Observation 259fe027-6058-4c1a-ab41-a41ba33e9bae · outbound

This paper cites Communication-efﬁcient learning of deep networks from decentralized data,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Communication-efﬁcient learning of deep networks from decentralized data,

Reference 11

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Observation ca673e63-b46c-4bed-a45a-74fbd6169947 · outbound

This paper cites A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications,

Reference 12

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Observation 9dd589fe-7e89-4a1f-9f21-32eac14fa181 · outbound

This paper cites Uav communications for 5g and beyond: Recent advances and future trends,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Uav communications for 5g and beyond: Recent advances and future trends,

Reference 13

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Observation ffe05449-d059-4e44-8c05-5a723c1e3667 · outbound

This paper cites Uav-enabled wireless power transfer: Trajectory design and energy optimization,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Uav-enabled wireless power transfer: Trajectory design and energy optimization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.396126Z

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Observation c9dd62a1-cf15-46e7-9ec1-cad8f5cf8006 · outbound

This paper cites Towards an Intelligent Edge: Wireless Communication Meets Machine Learning.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Towards an Intelligent Edge: Wireless Communication Meets Machine Learning

Reference 15

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Observation edd70ae9-fb8f-4082-87fc-6612f9966324 · outbound

This paper cites Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.372049Z

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Observation 33709ddd-e01f-4765-921c-d238ef257850 · outbound

This paper cites Revenue-driven service provisioning for resource sharing in mobile cloud computing,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Revenue-driven service provisioning for resource sharing in mobile cloud computing,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.348606Z

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Observation 229551d0-bc65-4d35-8731-6b6894cf2fe4 · outbound

This paper cites Deep reinforcement learning-based mode selection and resource management for green fog radio access networks,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Deep reinforcement learning-based mode selection and resource management for green fog radio access networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.360936Z

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Observation 1f8d5d27-ff49-4390-8ba6-c818a2ec914b · outbound

This paper cites Composition- driven iot service provisioning in distributed edges,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Composition- driven iot service provisioning in distributed edges,

Reference 20

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Observation 65559ebc-1caf-47ea-89ed-12182f266810 · outbound

This paper cites Mobility-aware service composition in mobile communities,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Mobility-aware service composition in mobile communities,

Reference 21

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Observation ca421ee9-0b35-4b4d-baae-2226ddeff6da · outbound

This paper cites Mobile service selection for composition: An energy consumption perspective,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Mobile service selection for composition: An energy consumption perspective,

Reference 22

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Observation c0cdecc8-da5d-4f2b-87d8-ee872c22d224 · outbound

This paper cites Cooperative edge caching in user-centric clustered mobile networks,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Cooperative edge caching in user-centric clustered mobile networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.302449Z

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Observation b11dfd91-286b-4782-9610-38bd9270728c · outbound

This paper cites Qoe aware and cell capacity enhanced computation ofﬂoading for multi-server mobile edge computing systems with energy harvesting devices,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Qoe aware and cell capacity enhanced computation ofﬂoading for multi-server mobile edge computing systems with energy harvesting devices,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.291520Z

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Observation 0a9336ac-744f-4300-9b24-db411b640a85 · outbound

This paper cites Modern service industry and crossover services: Development and trends in china,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Modern service industry and crossover services: Development and trends in china,

Reference 25

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Observation 03ca1c2f-711b-4dd0-84ec-3e5ef2547ac1 · outbound

This paper cites Learning- based computation ofﬂoading for iot devices with energy harvesting,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Learning- based computation ofﬂoading for iot devices with energy harvesting,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.267396Z

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Observation 1fdfc351-6039-4c74-a611-fa92a405f1be · outbound

This paper cites Perfor- mance optimization in mobile-edge computing via deep reinforcement learning,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Perfor- mance optimization in mobile-edge computing via deep reinforcement learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.256529Z

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Observation e426c7c5-9416-4595-9d52-3e28bc09b637 · outbound

This paper cites A mobility- aware cross-edge computation ofﬂoading framework for partitionable applications,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence A mobility- aware cross-edge computation ofﬂoading framework for partitionable applications,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.278484Z

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Observation b91b9ea1-2c26-4537-a2f9-94ada6d7d4ab · outbound

This paper cites A density-based ofﬂoading strategy for iot devices in edge computing systems,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence A density-based ofﬂoading strategy for iot devices in edge computing systems,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.245154Z

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Observation cd86db7a-a361-4728-bf35-81415d50a75a · outbound

This paper cites Wireless Network Intelligence at the Edge.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Wireless Network Intelligence at the Edge

Reference 30

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Observation 2d7b1b3b-09ff-4fab-bfbb-a9d18e99c35e · outbound

This paper cites Service selection for composition with qos correlations,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Service selection for composition with qos correlations,

Reference 31

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Observation f00e9aeb-aab5-46cb-a360-85f472ccec9b · outbound

This paper cites Edge intelligence: On-demand deep learning model co-inference with device-edge synergy,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Edge intelligence: On-demand deep learning model co-inference with device-edge synergy,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.234165Z

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Observation dbab3f87-97a0-4ac6-a5fb-39061e01faa7 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overﬁt- ting,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Dropout: A simple way to prevent neural networks from overﬁt- ting,

Reference 33

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Observation dba2a874-4d07-4f94-8a77-b45071354d6a · outbound

This paper cites Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.787402Z

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Observation 3d845c3c-9b33-4fe6-bfce-f9c69802aca4 · outbound

This paper cites Adaptive Precision CNN Accelerator Using Radix-X Parallel Connected Memristor Crossbars.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Adaptive Precision CNN Accelerator Using Radix-X Parallel Connected Memristor Crossbars

Reference 35

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Observation 6bf74710-4d51-451b-8314-870b78d11530 · outbound

This paper cites Over-the-air Function Computation in Sensor Networks.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Over-the-air Function Computation in Sensor Networks

Reference 36

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Observation 8b0d4ded-11f3-4b53-ab3f-a4a90c008950 · outbound

This paper cites Efﬁcient processing of deep neural networks: A tutorial and survey,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Efﬁcient processing of deep neural networks: A tutorial and survey,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.216324Z

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Observation 7428f8e2-0214-4c28-a1d5-9410f4845110 · outbound

This paper cites Federated Learning via Over-the-Air Computation.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Federated Learning via Over-the-Air Computation

Reference 39

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Observation 137fd3f1-0464-401c-9b42-ae7ee90649ff · outbound

This paper cites MIMO Over-the-Air Computation for High-Mobility Multi-Modal Sensing.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence MIMO Over-the-Air Computation for High-Mobility Multi-Modal Sensing

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.752289Z

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Observation 58cb90e1-2183-4e1d-96c5-ae8651ee3eb1 · outbound

This paper cites Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data

Reference 41

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Observation 238de2da-431d-4cd5-bbcf-23b2a3564b9f · outbound

This paper cites A Graph Neural Network Approach for Scalable Wireless Power Control.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence A Graph Neural Network Approach for Scalable Wireless Power Control

Reference 42

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Observation eee0e13d-e5f5-4f02-a39f-d09bc7ac9178 · outbound

This paper cites Dynamic Control of Functional Splits for Energy Harvesting Virtual Small Cells: a Distributed Reinforcement Learning Approach.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Dynamic Control of Functional Splits for Energy Harvesting Virtual Small Cells: a Distributed Reinforcement Learning Approach

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.682253Z

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Observation 642ad62a-dc60-435e-bb98-fe19b898f12a · outbound

This paper cites Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.718347Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3e14a0d7-c20c-4a13-8953-18ae67f0f3ce · outbound

This paper cites Data-intensive ap- plication deployment at edge: A deep reinforcement learning approach,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Data-intensive ap- plication deployment at edge: A deep reinforcement learning approach,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.205930Z

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Observation 467ac754-3543-4f5b-80b6-a6231c49a578 · outbound

This paper cites Deep reinforcement learning: A brief survey,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Deep reinforcement learning: A brief survey,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.195803Z

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Observation 68f11891-857c-4c93-96b2-42949e8f559e · outbound

This paper cites Online deep rein- forcement learning for computation ofﬂoading in blockchain-empowered mobile edge computing,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Online deep rein- forcement learning for computation ofﬂoading in blockchain-empowered mobile edge computing,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.185138Z

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Observation 011c6991-4e38-4f58-a7c9-66efa75b065b · outbound

This paper cites Spatiotemporal edge service placement: A bandit learning approach,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Spatiotemporal edge service placement: A bandit learning approach,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.325665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7ff2eb65-56da-4f5f-b7d6-e4318bf5fd35 · outbound

This paper cites Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-14T05:52:37.331542Z

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Observation b6536a2d-7025-4703-b90d-3d31540b4b50 · outbound

This paper cites Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.656818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation feb7590f-f568-48ae-ac22-5081c18655b4 · outbound

This paper cites Vehicular Edge Computing via Deep Reinforcement Learning.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Vehicular Edge Computing via Deep Reinforcement Learning

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.642239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1d5ae64b-904d-4ce1-8511-97717930b606 · outbound

This paper cites Optimized computation ofﬂoading performance in virtual edge computing systems via deep reinforcement learning,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Optimized computation ofﬂoading performance in virtual edge computing systems via deep reinforcement learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.174163Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 14df7fa9-498b-400b-a66e-324f99bb491a · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Federated Learning: Strategies for Improving Communication Efficiency

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-14T05:52:37.346954Z

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Observation b894c175-1ceb-4b82-8338-74f5375d7c74 · outbound

This paper cites Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-14T05:52:37.359544Z

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Observation 981746bf-0b59-43b9-9258-03408996a3a1 · outbound

This paper cites Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.626725Z

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Observation 0a0575c7-0c60-4cf0-940d-4997bc7f2e73 · outbound

This paper cites Run-Time Efficient RNN Compression for Inference on Edge Devices.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Run-Time Efficient RNN Compression for Inference on Edge Devices

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.580146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9f81cd68-6299-41f0-ba82-e0d963718e51 · outbound

This paper cites Diving deeper into mentee networks.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Diving deeper into mentee networks

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.564759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 90653d8c-e661-47b7-a97c-1bec4eea842a · outbound

This paper cites Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.549442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 99719565-eb24-44d3-8daf-8400737c0ef0 · outbound

This paper cites Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence

Reference 60

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.534173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8b6143f2-3916-4949-876a-93ce07ec4021 · outbound

This paper cites Strategies for re- training a pruned neural network in an edge computing paradigm,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Strategies for re- training a pruned neural network in an edge computing paradigm,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.162310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation df1f71ff-3ea0-4e2f-aada-d39660d61567 · outbound

This paper cites Toward Runtime-Throttleable Neural Networks.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Toward Runtime-Throttleable Neural Networks

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.518686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3e4eb8f5-de67-4b25-b2b7-fce0cd4b509b · outbound

This paper cites Gossip training for deep learning.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Gossip training for deep learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-14T05:52:37.390579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6245b5be-4bd8-4c91-8e35-398ae4b16925 · outbound

This paper cites GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-14T05:52:37.394417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba9521c1-b55d-40c6-a25c-2a6e4eabe5ad · outbound

This paper cites Protonn: Compressed and accurate knn for resource-scarce devices,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Protonn: Compressed and accurate knn for resource-scarce devices,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.151283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bc0ebf2c-4936-44f0-a08d-503ccc3d8a54 · outbound

This paper cites SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.452522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5d11ad02-f0ca-4958-862b-ea18d9116637 · outbound

This paper cites Stochastic neighbor compression,.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Stochastic neighbor compression,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.139202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2bcaf2f7-8500-4d90-a94b-240f6867d918 · outbound

This paper cites Blockchained On-Device Federated Learning.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Blockchained On-Device Federated Learning

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-14T05:52:37.402536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71a78c4a-f7e4-4246-8c36-1ad71f72ebc2 · outbound

This paper cites Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:52:37.467859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 54eeff24-e0ce-4a7b-a0ed-e4f16a0914b4 · outbound

This paper cites Practical Secure Aggregation for Federated Learning on User-Held Data.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Practical Secure Aggregation for Federated Learning on User-Held Data

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-14T05:52:37.355132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39a66afc-4f3b-491d-8136-caba918aa0c4 · outbound

This paper cites Available: https://doi.org/10.1109/TASE.2015.2438020.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Available: https://doi.org/10.1109/TASE.2015.2438020

Reference 2017

Resolution: metadata mismatch; raw_fallback, observed 2026-08-14T05:52:38.065153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cc47f48b-b2e6-444e-b9c8-c9cf3ec3e7fe · outbound

This paper cites Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version).

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-14T05:52:37.291449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ddddf2f-ad8c-4265-be9c-588e6364f928 · outbound

This paper cites Available: https://www.ericsson.com/en/mobility-report/ reports/june-2019/iot-connections-outlook.

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence Available: https://www.ericsson.com/en/mobility-report/ reports/june-2019/iot-connections-outlook

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:52:38.466701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Outbound references

Pith citing papers