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FLight: A lightweight federated learning framework in edge and fog computing
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Cloud Computing and Internet of Things: Recent Trends and Directions
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Making resource adaptive to federated learning with COTS mobile devices
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A computation offloading method over big data for IoT-enabled cloud-edge computing
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Joint resource management for mobility supported federated learning in Internet of Vehicles
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GDPR principles in Data protection encourage pseudonymization through most popular and full-personalized devices - mobile phones
inferredcites →https://doi.org/10.1109/access.2018.2830661openalex-snapshot
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
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Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
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Jointly Optimizing Client Selection and Resource Management in Wireless Federated Learning for Internet of Things
inferredcites →https://doi.org/10.1109/jiot.2022.3176469openalex-snapshot
FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning
inferredcites →https://doi.org/10.1109/jsac.2019.2904348openalex-snapshot
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
inferredcites →https://doi.org/10.1109/jsac.2021.3088655openalex-snapshot
Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management
inferredcites →https://doi.org/10.1109/lwc.2022.3149783openalex-snapshot
Federated Dropout—A Simple Approach for Enabling Federated Learning on Resource Constrained Devices
inferredcites →https://doi.org/10.1109/msec.2018.2888775openalex-snapshot
Privacy-Preserving Machine Learning: Threats and Solutions
inferredcites →https://doi.org/10.1109/msp.2012.2211477openalex-snapshot
The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
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A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
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An Application Placement Technique for Concurrent IoT Applications in Edge and Fog Computing Environments
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A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments
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Eiffel : Efficient and Fair Scheduling in Adaptive Federated Learning
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A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks
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An Introduction to the Federated Learning Standard
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Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions
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The California Consumer Privacy Act (CCPA)
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Federated Learning Architectures: A Performance Evaluation With Crop Yield Prediction Application
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On‐Demand Deployment of Edge Cloud Infrastructures for Federated Learning
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MO-BMB for Multi-objective Task Offloading Optimization in Fog-Cloud Environment
inferred← citeshttps://doi.org/10.1007/s42979-026-05078-2openalex-snapshot
A Multi-objective Optimized Fog-Coordinated Federated Edge AI Framework for IoT Analytics
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Air Quality Monitoring in Sparse Urban Networks: An Enhanced Fog Hierarchical Federated Learning Approach
inferred← citeshttps://doi.org/10.1016/j.future.2023.10.012openalex-snapshot
Deep Reinforcement Learning-based scheduling for optimizing system load and response time in edge and fog computing environments
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Small models, big impact: A review on the power of lightweight Federated Learning
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Reliable and privacy-preserving collaborative AI in multi-tier distributed architectures
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PLATFL: Privacy-Preserving Lightweight Asynchronous Trust-Based Federated Learning Framework for IoT Anomaly Detection
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Adaptive Task Scheduling in Fog Computing Using Federated DQN and K-Means Clustering
inferred← citeshttps://doi.org/10.1109/access.2026.3650945openalex-snapshot
Energy-Efficient Federated Learning Scheduler for VM Allocation in Edge-Cloud Continuum
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Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
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Revolutionizing Cloud Computing: Decentralized Scheduling with Federated Learning for Enhanced Resource Utilization
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A Review on Latency-Aware Task Scheduling in Fog-Cloud IoT Systems Addressing Challenges and Future Directions
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Chaos Engineering and Observability Integration for Resilient Cloud Infrastructure
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Blockchain-Augmented FL IDS for Non-IID Edge-IoT Data Using Trimmed Mean Aggregation
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Industrial Process Automation Through Machine Learning and OPC-UA: A Systematic Literature Review
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Priority-Aware Multi-Objective Task Scheduling in Fog Computing Using Simulated Annealing
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A Federated Learning Autoencoder CNN Hybrid Approach for Privacy Preserving Network Traffic Classification
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