相关论文: Federated Learning for Smart Grid: A Survey on App…
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 data-driven models often require huge amounts of data to…
With increasing concerns for data privacy and ownership, recent years have witnessed a paradigm shift in machine learning (ML). An emerging paradigm, federated learning (FL), has gained great attention and has become a novel design for…
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data…
In the recent years, generation of data have escalated to extensive dimensions and big data has emerged as a propelling force in the development of various machine learning advances and internet-of-things (IoT) devices. In this regard, the…
Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the growth of IoT devices, vast quantities of data that…
Federated Learning (FL) has emerged as a transformative paradigm in the field of distributed machine learning, enabling multiple clients such as mobile devices, edge nodes, or organizations to collaboratively train a shared global model…
With the deployment of smart sensors and advancements in communication technologies, big data analytics have become vastly popular in the smart grid domain, informing stakeholders of the best power utilization strategy. However, these…
Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy…
Federated learning (FL) refers to a distributed machine learning framework involving learning from several decentralized edge clients without sharing local dataset. This distributed strategy prevents data leakage and enables on-device…
Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of wireless systems, such as sixth-generation (6G) mobile network. However, massive data, energy consumption, training complexity, and sensitive…
Federated learning (FL) is a system in which a central aggregator coordinates the efforts of multiple clients to solve machine learning problems. This setting allows training data to be dispersed in order to protect privacy. The purpose of…
With growing security and privacy concerns in the Smart Grid domain, intrusion detection on critical energy infrastructure has become a high priority in recent years. To remedy the challenges of privacy preservation and decentralized power…
Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising…
Federated learning (FL) is a kind of distributed machine learning framework, where the global model is generated on the centralized aggregation server based on the parameters of local models, addressing concerns about privacy leakage caused…
The widespread use of the Internet of Things has led to the development of large amounts of perception data, making it necessary to develop effective and scalable data analysis tools. Federated Learning emerges as a promising paradigm to…
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may…
Federated Learning (FL) is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While FL enhances data privacy by design, it remains vulnerable…
Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) are used to record household energy consumption. Traditional…
Federated Learning (FL) has been recently proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing…
Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications. In the context of an Internet of Things (IoT) ecosystem, the data communication is frequently costly, inefficient, not…