Adaptive resource aware and privacy preserving federated edge learning framework for real time internet of medical things applications

Nature·Oct 20

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Researchers have developed various frameworks for federated edge learning, focusing on aspects such as privacy preservation, resource awareness, and adaptability in internet of things applications. These frameworks aim to address different challenges, including concept drift, sentiment analysis, and anomaly detection, in various environments, such as industrial networks and educational settings. The proposed solutions incorporate techniques like differential privacy, blockchain integration, and reinforcement learning to ensure secure and efficient edge intelligence.

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FedDriftGuard adaptive federated learning with differential privacy for concept drift in edge environments

Nature·May 2

FedEnsemble: federated learning model for efficient sentiment analysis

Springer Nature Link·Dec 26

SecuFL-IoT: an adaptive privacy-preserving federated learning framework for anomaly detection in smart industrial networks

Nature·Jan 29

Negotiation-augmented federated reinforcement learning for conflict-free edge–cloud stream scheduling

Nature·Mar 26

Secure blockchain integrated deep learning framework for federated risk-adaptive and privacy-preserving IoT edge intelligence sets

Nature·Nov 20

LaED: a novel lightweight, edge-aware and explainable deep learning model for privacy-preserving facial attendance tracking in resource-constrained educational environments

Nature·May 15

A federated edge intelligence framework with trust based access control for secure and privacy preserving IoT systems

Nature·Oct 14

An Adaptive Blockchain Framework for Federated IoMT with Reinforcement Learning-Based Consensus and Resource Forecasting

Nature·Feb 11

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