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Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection
In recent years, several quantum GAN architectures have been proposed in the literature.
Towards a Secure and Reliable Federated Learning using Blockchain
Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy.
Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning
In addition to preserving prosumers' privacy, we show through evaluations that training prediction models using Federated Learning yields high accuracy for different energy resources while reducing the communication overhead.
Data-Aware Device Scheduling for Federated Edge Learning
As the data is the key component of the learning, we propose a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL.
Federated Edge Learning : Design Issues and Challenges
The design of FEEL algorithms for resources-efficient learning raises several challenges.
