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Edge, Fog, and Cloud Computing : An Overview on Challenges and Applications

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arxiv 2211.01863 v1 pith:R3HA6UYY submitted 2022-11-03 cs.DC

classification cs.DC
keywords cloudcomputingapplicationschallengesedgesmartthoseagriculture
verification ladder T0 review T1 audit T2 compute T3 formal

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With the rapid growth of the Internet of Things (IoT) and a wide range of mobile devices, the conventional cloud computing paradigm faces significant challenges (high latency, bandwidth cost, etc.). Motivated by those constraints and concerns for the future of the IoT, modern architectures are gearing toward distributing the cloud computational resources to remote locations where most end-devices are located. Edge and fog computing are considered as the key enablers for applications where centralized cloud-based solutions are not suitable. In this paper, we review the high-level definition of edge, fog, cloud computing, and their configurations in various IoT scenarios. We further discuss their interactions and collaborations in many applications such as cloud offloading, smart cities, health care, and smart agriculture. Though there are still challenges in the development of such distributed systems, early research to tackle those limitations have also surfaced.

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Cited by 1 Pith paper

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  1. LLMs meet Federated Learning for Scalable and Secure IoT Management

    cs.LG 2025-04 reject novelty 3.0 of 10

    A threshold-based asynchronous federated learning strategy for fine-tuning LLMs on IoT data reports modest accuracy gains and large latency/throughput wins over FedAvg and FedOpt on the IoT-23 dataset.

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