Non-intrusive Load Monitoring (NILM) is becoming a paramount in both industrial and residential sectors to achieve efficient energy consumption. Thus, research on this matter flourished in recent years, where deep neu...
详细信息
ISBN:
(纸本)9781665492669
Non-intrusive Load Monitoring (NILM) is becoming a paramount in both industrial and residential sectors to achieve efficient energy consumption. Thus, research on this matter flourished in recent years, where deep neural networks gained the highest interest from the research community, commonly referred to as neural NILM. As a predominant practice, neural NILM models follow a centralised based learning scheme where the energy data is assumed to be available in a central node for training. However, this practice and the enormous amount of data required by these algorithms raise privacy and security concerns from the consumer’s side since energy data can reveal in-home activities and occupancy records if intercepted. Federated Learning (FL), also referred to as collaborative learning, is seen as viable solution to address these issues. Nonetheless, its application in neural NILM is still in its infancy and many challenges are yet to be *** current paper presents an overview of neural NILM models following both a centralised and a federated learning paradigm. Furthermore, it identifies the main challenges with regard to both learning paradigms along with potential future research directions for more robust, secure and privacy-preserving models in the neural NILM industry.
Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resourc...
详细信息
Privacy concerns are widely discussed in research and society in general. For the public infrastructure of financial blockchains, this discussion encompasses the privacy of the originator of a transaction broadcasted ...
详细信息
To obtain a knowledge graph representing a domain of interest, it is often necessary to combine several, independently developed ontologies. Existing approaches are mostly limited to binary merge and lack scalability....
详细信息
We present a partitioned neural network-based framework for learning of fluid-structure interaction (FSI) problems. We decompose the simulation domain into two smaller sub-domains, i.e., fluid and solid domains, and i...
详细信息
Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress in deep neural network architectural...
详细信息
As a cutting-edge technology of low-altitude Artificial Intelligence of Thing (AIoT), UAV object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of...
详细信息
Consumers frequently interact with reputation systems to rate products, services, and deliveries. While past research extensively studied different conceptual approaches to realize such systems securely and privacy-pr...
详细信息
The massive upsurge in cloud resource demand and inefficient load management stave off the sustainability of Cloud Data Centres (CDCs) resulting in high energy consumption, resource contention, excessive carbon emissi...
Ontology merging systems enable the reusability and interoperability of existing knowledge. Ideally, they allow their users to specify which characteristics the merged ontology should have. In prior work, we have iden...
详细信息
暂无评论