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Federated Learning Architectures: A Performance Evaluation With Crop Yield Prediction Application

https://doi.org/10.1002/spe.3416
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/spe.3300openalex-snapshot
FLight: A lightweight federated learning framework in edge and fog computing
inferredcites →https://doi.org/10.1002/spe.3324openalex-snapshot
Fly: Femtolet‐based edge‐cloud framework for crop yield prediction using bidirectional long short‐term memory
inferredcites →https://doi.org/10.1007/978-981-99-0085-5_68openalex-snapshot
Smart Crop Recommendation System: A Machine Learning Approach for Precision Agriculture
inferredcites →https://doi.org/10.1007/s11042-023-16113-2openalex-snapshot
Red fox optimization with ensemble recurrent neural network for crop recommendation and yield prediction model
inferredcites →https://doi.org/10.1007/s11227-023-05131-4openalex-snapshot
E-CropReco: a dew-edge-based multi-parametric crop recommendation framework for internet of agricultural things
inferredcites →https://doi.org/10.1016/j.atech.2023.100277openalex-snapshot
Federated Learning: Crop classification in a smart farm decentralised network
inferredcites →https://doi.org/10.1016/j.cie.2020.106854openalex-snapshot
A review of applications in federated learning
inferredcites →https://doi.org/10.1016/j.compag.2019.104968openalex-snapshot
A novel approach for efficient crop yield prediction
inferredcites →https://doi.org/10.1016/j.compag.2020.105709openalex-snapshot
Crop yield prediction using machine learning: A systematic literature review
inferredcites →https://doi.org/10.1016/j.compag.2021.106648openalex-snapshot
The role of cross-silo federated learning in facilitating data sharing in the agri-food sector
inferredcites →https://doi.org/10.1016/j.eswa.2023.122847openalex-snapshot
Model pruning enables localized and efficient federated learning for yield forecasting and data sharing
inferredcites →https://doi.org/10.1016/j.future.2020.10.007openalex-snapshot
A survey on security and privacy of federated learning
inferredcites →https://doi.org/10.1016/j.future.2023.04.034openalex-snapshot
Federated deep learning for smart city edge-based applications
inferredcites →https://doi.org/10.1016/j.future.2025.108295openalex-snapshot
A joint time and energy-efficient federated learning-based computation offloading method for mobile edge computing
inferredcites →https://doi.org/10.1016/j.iot.2020.100187openalex-snapshot
Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review
inferredcites →https://doi.org/10.1016/j.iot.2021.100378openalex-snapshot
Data management and internet of things : A methodological review in smart farming
inferredcites →https://doi.org/10.1016/j.iot.2023.100845openalex-snapshot
FedMicro-IDA: A federated learning and microservices-based framework for IoT data analytics
inferredcites →https://doi.org/10.1016/j.jpdc.2022.03.003openalex-snapshot
FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things
inferredcites →https://doi.org/10.1016/j.knosys.2021.106775openalex-snapshot
A survey on federated learning
inferredcites →https://doi.org/10.1109/comst.2021.3075439openalex-snapshot
Federated Learning for Internet of Things: A Comprehensive Survey
inferredcites →https://doi.org/10.1109/tce.2024.3370373openalex-snapshot
FLAG: Federated Learning for Sustainable Irrigation in Agriculture 5.0
inferredcites →https://doi.org/10.1109/tce.2024.3440931openalex-snapshot
Fedchain: Decentralized Federated Learning and Blockchain-Assisted System for Sustainable Irrigation
inferredcites →https://doi.org/10.14209/sbrt.2022.1570824835openalex-snapshot
An IoT Crop Recommendation System with k-NN and LoRa for Precision Farming
inferredcites →https://doi.org/10.3390/s22166299openalex-snapshot
A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming
inferredcites →https://doi.org/10.3390/technologies10010013openalex-snapshot
IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/spe.3439openalex-snapshot
Early Response Framework for Accident Detection and Prevention Through Multi‐Zone Fog‐Cloud Collaboration for Safety‐Critical Applications
inferred← citeshttps://doi.org/10.1007/978-3-032-22190-2_29openalex-snapshot
A Comprehensive Review of Machine Learning Algorithms for Crop Yield Prediction in Smart Agriculture: State-of-the-Art Methods, Challenges, and Future Directions
inferred← citeshttps://doi.org/10.1007/s10462-026-11629-zopenalex-snapshot
Trends, challenges and future directions in deep learning for citrus leaf disease recognition: a survey
inferred← citeshttps://doi.org/10.1007/s10586-026-06633-5openalex-snapshot
A novel federated learning approach for enhancing QoS and scalability in IoT networks
inferred← citeshttps://doi.org/10.1007/s10791-026-10291-xopenalex-snapshot
Federated agricultural yield forecasting under an Edge–Fog–Cloud framework: a multi-country privacy-preserving approach
inferred← citeshttps://doi.org/10.1007/s10791-026-10549-4openalex-snapshot
A systematic literature review of artificial intelligence and internet of things enabled crop farming
inferred← citeshttps://doi.org/10.1007/s13562-026-01064-0openalex-snapshot
Precision under pressure: leveraging interpretable AI and machine learning to predict multifactorial abiotic stress in climate-smart crops
inferred← citeshttps://doi.org/10.1016/b978-0-44-344433-3.00006-xopenalex-snapshot
Federated learning at a glance
inferred← citeshttps://doi.org/10.1016/j.mex.2025.103408openalex-snapshot
Federated learning for crop yield prediction: A comprehensive review of techniques and applications
inferred← citeshttps://doi.org/10.1016/j.procs.2025.09.437openalex-snapshot
An Overview of Blockchain-Enabled Federated Learning Architectures for Agricultural Applications
inferred← citeshttps://doi.org/10.1109/ccci65983.2025.11215110openalex-snapshot
FedDP: Differential Privacy-based Federated Learning for Smart Soil Health Prediction
inferred← citeshttps://doi.org/10.1109/ic3sis69949.2026.11608334openalex-snapshot
A Survey of Machine Learning and Deep Learning Approaches for Crop Yield Prediction: Methods, Data Sources, and Open Challenges
inferred← citeshttps://doi.org/10.1109/iceca66444.2025.11383309openalex-snapshot
Edge Computing based Cubic Gradient Deep Multilayer Perceptron for Global Warming Prediction on Crop Yield
inferred← citeshttps://doi.org/10.1109/icesic67389.2026.11496546openalex-snapshot
Integrated IoT and Deep Learning Architecture for Real-Time Crop Stress Detection and Irrigation Optimization
inferred← citeshttps://doi.org/10.1109/iciccs67901.2026.11502960openalex-snapshot
Federated Deep Learning Framework for Multimodal Crop Yield Prediction using Satellite Imagery and Environmental Data
inferred← citeshttps://doi.org/10.1109/icoiics67115.2025.11390359openalex-snapshot
Advanced Crop Yield Prediction through Granger Causality-Inspired Graph Neural Network Optimized with Serial Exponential Coati Optimization Algorithm
inferred← citeshttps://doi.org/10.1109/icsseecc69062.2026.11722139openalex-snapshot
AgriAlign: An Intelligent Precision Sowing and Predictive Agro-Analytics Framework Using IoT-Driven Motion Intelligence and Machine Learning
inferred← citeshttps://doi.org/10.1109/indicon68490.2025.11392876openalex-snapshot
Privacy-enhanced and Time-efficient Human Activity Recognition using Decentralized Federated Learning
inferred← citeshttps://doi.org/10.1109/jiot.2025.3558910openalex-snapshot
Optimizing Federated Learning: Addressing Key Challenges in Real-World Applications
inferred← citeshttps://doi.org/10.1109/miot.2025.3643736openalex-snapshot
Differential Privacy-Based Lightweight Federated Learning Framework for Internet of Agricultural Things
inferred← citeshttps://doi.org/10.1109/wincom65874.2025.11313439openalex-snapshot
Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
inferred← citeshttps://doi.org/10.11648/j.ajese.20261004.11openalex-snapshot
Smart Agriculture and Environmental Monitoring: An Edge-AI, IoT, and Federated Learning Framework for Climate-Resilient Farming
inferred← citeshttps://doi.org/10.3390/eng7020082openalex-snapshot
Implementation of a Wireless Sensor Network for Agro-Environmental Monitoring and Growing Degree Day-Based Rice Growth Assessment
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