In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Since the energy sector is the dominant contributor to global greenhouse gas emissions, the
decarbonization of energy systems is crucial for climate change mitigation. Two major …

As a crucial role in the prognostic and health management of mechanical equipment, fault
diagnosis encounters serious challenges, such as the scarcity of fault samples, the high cost …

Decentralized and asynchronous communications are two popular techniques to speedup
communication complexity of distributed machine learning, by respectively removing the …

The energy losses and costs associated with faults in photovoltaic (PV) systems significantly
limit the efficiency and reliability of solar power. Since existing methods for automatic fault …

Federated Learning is a promising technique that facilitates cloud–edge–terminal
collaboration in Artificial Intelligence of Things (AIoT). It will enable model training without …

Federated learning is an emerging technology that enables multiple clients to cooperatively
train an intelligent diagnostic model while preserving data privacy. However, federated …

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy
data, particularly when used to train machine learning models for different services. These …

Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …

Federated learning (FL) has recently gained popularity as a distributed machine learning
approach that protects privacy. However, this concept has not yet been extensively adopted …