Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Heterogeneity across clients in federated learning (FL) usually hinders the optimization
convergence and generalization performance when the aggregation of clients' knowledge …

Neural architecture search (NAS) has become increasingly popular in the deep learning
community recently, mainly because it can provide an opportunity to allow interested users …

Federated learning (FL) is a rapidly growing research field in machine learning. However,
existing FL libraries cannot adequately support diverse algorithmic development; …

Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

Federated learning (FL) is a training paradigm where the clients collaboratively learn
models by repeatedly sharing information without compromising much on the privacy of their …

Wind farms are typically located at high latitudes, resulting in a high risk of blade icing. Data-
driven approaches offer promising solutions for blade icing detection, but they rely on a …

Federated learning (FL) is an efficient learning framework that assists distributed machine
learning when data cannot be shared with a centralized server due to privacy and regulatory …

With the development of edge devices and cloud computing, the question of how to
accomplish machine learning and optimization tasks in a privacy-preserving and secure way …

Federated Learning (FL) provides both model performance and data privacy for machine
learning tasks where samples or features are distributed among different parties. In the …