Personalized client-edge-cloud hierarchical federated learning in mobile edge computing by Chunmei Ma, Xiangqian Li, Baogui Huang, Guangshun Li, Fengyin Li

“…However, a lot of research results exhibit three distinct limitations: 1) suboptimal communication efficiency, 2) slow model convergence, and 3) underutilization of the relationships within user data, resulting in lower accuracy of personalized models. In this paper, we present the first personalized federated learning algorithm based on the client-edge-cloud structure. …”
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Randomized Quantization for Privacy in Resource Constrained Machine Learning at-the-Edge and Federated Learning by Ce Feng, Parv Venkitasubramaniam

“…The increasing adoption of machine learning at the edge (ML-at-the-edge) and federated learning (FL) presents a dual challenge: ensuring data privacy as well as addressing resource constraints such as limited computational power, memory, and communication bandwidth. …”
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Edge Cloud Resource Scheduling with Deep Reinforcement Learning by Y. Feng, M. Li, J. Li, Y. Yu

“…Designing optimal scheduling algorithms for task allocation in edge cloud clusters presents significant challenges due to the constantly changing workloads and service requests in edge cloud data center environments. …”
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Deploying AI on Edge: Advancement and Challenges in Edge Intelligence by Tianyu Wang, Jinyang Guo, Bowen Zhang, Ge Yang, Dong Li

“…This article systematically reviews the current advancements in edge intelligence technologies, highlights key enabling techniques including model sparsity, quantization, knowledge distillation, neural architecture search, and federated learning, and explores their applications in industrial, automotive, healthcare, and consumer domains. …”
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Enabling All In-Edge Deep Learning: A Literature Review by Praveen Joshi, Mohammed Hasanuzzaman, Chandra Thapa, Haithem Afli, Ted Scully

“…Secondly, this paper presents enabling technologies, such as model parallelism, data parallelism, and split learning, which facilitates DL training and deployment at edge servers. …”
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Emotion on the edge: An evaluation of feature representations and machine learning models by James Thomas Black, Muhammad Zeeshan Shakir

“…We examine the training and inference times of models to determine the most efficient combination when employing an edge architecture, investigating each model’s performance from training to inference using an edge board. …”
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Intelligent Infrastructure for Traffic Monitoring Based on Deep Learning and Edge Computing by Jaime Villa, Franz García, Rubén Jover, Ventura Martínez, José M. Armingol

“…These technologies allow for monitoring tasks without the need to install numerous sensors or stop the traffic, using the extensive camera network of surveillance cameras already present in worldwide roads. This study proposes a computer vision-based solution that allows for real-time processing of video streams through edge computing devices, eliminating the need for Internet connectivity or dedicated sensors. …”
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Federated Learning for a Dynamic Edge: A Modular and Resilient Approach by Leonardo Almeida, Rafael Teixeira, Gabriele Baldoni, Mário Antunes, Rui L. Aguiar

“…The increasing demand for distributed machine learning like Federated Learning (FL) in dynamic, resource-constrained edge environments, 5G/6G networks, and the proliferation of mobile and edge devices, presents significant challenges related to fault tolerance, elasticity, and communication efficiency. …”
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Enhancing Incentive Schemes in Edge Computing through Hierarchical Reinforcement Learning by Gowtham R, Vatsala Anand, Yadati Vijaya Suresh, Kasetty Lakshmi Narasimha, R. Anil Kumar, V. Saraswathi

“…This work contributes to the advancement of edge learning by presenting a reliable and efficient solution for real-world applications. …”
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Dynamic Edge Loading Balancing with Edge Node Activity Prediction and Accelerating the Model Convergence by Wen Chen, Sibin Liu, Yuxiao Yang, Wenjing Hu, Jinming Yu

“…However, the lack of a priori knowledge regarding the current load state of edge nodes for user devices presents a significant challenge in multi-user, multi-edge node scenarios. …”
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Decentralized queue control with delay shifting in edge-IoT using reinforcement learning by Viacheslav Kovtun

“…Abstract The article presents an adaptive approach to modelling and managing the service process of requests at peripheral nodes of edge-IoT systems. …”
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Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction by Kuo-Yang Huang, Ying-Lin Hsu, Che-Liang Chung, Huang-Chi Chen, Ming-Hwarng Horng, Ching-Hsiung Lin, Ching-Sen Liu, Jia-Lang Xu

“…Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient care. This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators. …”
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Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT by S. Benila, K. Devi

“…This novel approach integrates hierarchical reinforcement learning with multi-agent federated learning to enhance decision-making in dynamic edge computing environments. …”
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FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing by Kangning Yin, Xinhui Ji, Yan Wang, Zhiguo Wang

“…Federated learning (FL) is a distributed machine learning paradigm for edge cloud computing. …”
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Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning by Zhenwu Lei, Yue Zhang, Jing Wang, Meng Zhou

“…This paper addresses the problem of insufficient detection accuracy of existing lightweight models on resource-constrained edge devices by presenting a new lightweight YoloV5 model, which integrates four modules, SCDown, GhostConv, RepNCSPELAN4, and ScalSeq. …”
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Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…Furthermore, the study presents a multi-tier federated edge learning architecture that integrates cloud collaboration with edge servers to manage the increasing number of industrial devices and the demand for timely local model training. …”
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Federated Learning Framework for Real-Time Activity and Context Monitoring Using Edge Devices by Rania A. Alharbey, Faisal Jamil

“…With the increasing need for effective elderly care solutions, this paper presents a novel federated learning-based system that uses smartphones as edge devices to monitor and enhance elderly care in real-time. …”
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Pothole Detection Using Deep Learning: A Real-Time and AI-on-the-Edge Perspective by Muhammad Haroon Asad, Saran Khaliq, Muhammad Haroon Yousaf, Muhammad Obaid Ullah, Afaq Ahmad

“…In this work, we have exploited the AI kit (OAK-D) on a single-board computer (Raspberry Pi) as an edge platform for pothole detection. Detailed real-time performance comparison of state-of-the-art deep learning models and object detection frameworks (YOLOv1, YOLOv2, YOLOv3, YOLOv4, Tiny-YOLOv4, YOLOv5, and SSD-mobilenetv2) for pothole detection is presented. …”
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FedEmerge: An Entropy-Guided Federated Learning Method for Sensor Networks and Edge Intelligence by Koffka Khan

“…Evaluations on CIFAR-10 (non-IID), Federated EMNIST, and Shakespeare datasets confirm its effectiveness in practical edge-learning settings. <b>Conclusions:</b> This entropy-guided federated strategy demonstrates that weighting client updates by data diversity enhances learning outcomes in heterogeneous networks. …”
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Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing by Nada Alasbali, Jawad Ahmad, Ali Akbar Siddique, Oumaima Saidani, Alanoud Al Mazroa, Asif Raza, Rahmat Ullah, Muhammad Shahbaz Khan

“…Most existing automated detection/classification approaches that utilize machine learning or deep learning poses privacy issues, as they involve centralized computing and require local storage for data training.MethodsKeeping the privacy of sensitive patient data as a primary objective, in addition to ensuring accuracy and efficiency, this paper presents an algorithm that integrates Federated learning techniques into an IoT-based edge-computing environment. …”
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