As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Federated Learning (FL) is a collaboratively decentralized privacy-preserving technology to
overcome challenges of data silos and data sensibility. Exactly what research is carrying the …

Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …

Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …

PySyft is an open-source multi-language library enabling secure and private machine
learning by wrap** and extending popular deep learning frameworks such as PyTorch in …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

Multiple medical institutions collaboratively training a model using federated learning (FL)
has become a promising solution for maximizing the potential of data-driven models, yet the …

Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …