Cover Image

Optimizing Resource Allocation and Energy Efficiency in Federated Fog Computing for IoT

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shah, Syed Sarmad, Ali, Anas
Format:	Preprint
Published:	2025
Subjects:	Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions
by: Pallewatta, Samodha, et al.
Published: (2022)

Resource Allocation of Industry 4.0 Micro-Service Applications across Serverless Fog Federation
by: Hussain, Razin Farhan, et al.
Published: (2024)

FedFog: Resource-Aware Federated Learning in Edge and Fog Networks
by: Sobati-M, Somayeh
Published: (2025)

A Study to Optimize Heterogeneous Resources for Open IoT
by: Yamato, Yoji, et al.
Published: (2017)

Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices
by: Compaoré, Wilfrid Sougrinoma, et al.
Published: (2025)

Resource Slicing through Intelligent Orchestration of Energy-aware IoT services in Edge-Cloud Continuum
by: Shahid, Hafiz Faheem, et al.
Published: (2024)

Observability in Fog Computing
by: Araujo, Aleteia, et al.
Published: (2024)

Optimizing Malware Detection in IoT Networks: Leveraging Resource-Aware Distributed Computing for Enhanced Security
by: Kasarapu, Sreenitha, et al.
Published: (2024)

Edge System Design Using Containers and Unikernels for IoT Applications
by: Kaiser, Shahidullah, et al.
Published: (2024)

Federated Semi-Supervised and Semi-Asynchronous Learning for Anomaly Detection in IoT Networks
by: Zhai, Wenbin, et al.
Published: (2023)

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT
by: Ju, Zehao, et al.
Published: (2024)

Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
by: Chu, Shunfeng, et al.
Published: (2024)

Optimized Cloud Resource Allocation Using Genetic Algorithms for Energy Efficiency and QoS Assurance
by: Panggabean, Caroline, et al.
Published: (2025)

Jiagu: Optimizing Serverless Computing Resource Utilization with Harmonized Efficiency and Practicability
by: Liu, Qingyuan, et al.
Published: (2024)

Energy-aware Incremental OTA Update for Flash-based Batteryless IoT Devices
by: Wei, Wei, et al.
Published: (2024)

Benchmarking Message Brokers for IoT Edge Computing: A Comprehensive Performance Study
by: Paul, Tapajit Chandra, et al.
Published: (2026)

QEdgeProxy: QoS-Aware Load Balancing for IoT Services in the Computing Continuum
by: Čilić, Ivan, et al.
Published: (2024)

FOGNITE: Federated Learning-Enhanced Fog-Cloud Architecture
by: Sobati-M, Somayeh
Published: (2025)

Efficient Fog Node Placement using Nature-Inspired Metaheuristic for IoT Applications
by: Naouri, Abdenacer, et al.
Published: (2023)

Energy-Efficient Joint Offloading and Resource Allocation for Deadline-Constrained Tasks in Multi-Access Edge Computing
by: Gao, Chuanchao, et al.
Published: (2025)

Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT
by: Wang, Heqiang, et al.
Published: (2024)

Efficient Location-Based Service Discovery for IoT and Edge Computing in the 6G Era
by: Horvath, Kurt, et al.
Published: (2025)

ReinFog: A Deep Reinforcement Learning Empowered Framework for Resource Management in Edge and Cloud Computing Environments
by: Wang, Zhiyu, et al.
Published: (2024)

Enhancing IoT Malware Detection through Adaptive Model Parallelism and Resource Optimization
by: Kasarapu, Sreenitha, et al.
Published: (2024)

SchEdge: A Dynamic, Multi-agent, and Scalable Scheduling Simulator for IoT Edge
by: Hamedi, Ali, et al.
Published: (2025)

Decentralized Proactive Model Offloading and Resource Allocation for Split and Federated Learning
by: Huang, Binbin, et al.
Published: (2024)

EdgeLinker: Practical Blockchain-based Framework for Healthcare Fog Applications to Enhance Security in Edge-IoT Data Communications
by: Zarkesh, Mahdi Akbari, et al.
Published: (2024)

Energy-efficient Federated Learning with Dynamic Model Size Allocation
by: Kumar, M S Chaitanya, et al.
Published: (2024)

FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems
by: Nguyen, Van-Dinh, et al.
Published: (2021)

IoT-Based Wireless Networkingfor Seismic Applications
by: Jamali-Rad, Hadi, et al.
Published: (2024)

Privacy-Preserving and Trustworthy Localization in an IoT Environment
by: Zocca, Guglielmo, et al.
Published: (2024)

Performance and Security Aware Distributed Service Placement in Fog Computing
by: Goudarzi, Mohammad, et al.
Published: (2026)

Achieving Observability on Fog Computing with the use of open-source tools
by: Costa, Breno, et al.
Published: (2024)

A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
by: Wang, Zhiyu, et al.
Published: (2025)

TrustMesh: A Blockchain-Enabled Trusted Distributed Computing Framework for Open Heterogeneous IoT Environments
by: Rangwala, Murtaza, et al.
Published: (2024)

A Multi-Simulation Bridge for IoT Digital Twins
by: Picone, Marco, et al.
Published: (2025)

A Survey on Application Layer Protocols for IoT Networks
by: Hmissi, Fatma, et al.
Published: (2024)

A High-throughput and Secure Coded Blockchain for IoT
by: Taherpour, Amirhossein, et al.
Published: (2023)

Optimising Blockchain Scalability for Real-Time IoT Applications
by: Rhidoy, Hasan Mahmud, et al.
Published: (2026)

Cloud Resource Allocation with Convex Optimization
by: Boghani, Shayan, et al.
Published: (2025)