Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

Federated learning (FL) is a distributed machine learning approach that can achieve the
purpose of collaborative learning from a large amount of data that belong to different parties …

Federated Learning (FL) is a machine learning setting where many devices collaboratively
train a machine learning model while keeping the training data decentralized. In most of the …

Scaling up the convolutional neural network (CNN) size (eg, width, depth, etc.) is known to
effectively improve model accuracy. However, the large model size impedes training on …

Since edge device failures (ie, anomalies) seriously affect the production of industrial
products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming …

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

Federated learning involves training statistical models over remote devices or siloed data
centers, such as mobile phones or hospitals, while keeping data localized. Training in …

Open banking enables individual customers to own their banking data, which provides
fundamental support for the boosting of a new ecosystem of data marketplaces and financial …

In-home health monitoring has attracted great attention for the ageing population worldwide.
With the abundant user health data accessed by Internet of Things (IoT) devices and recent …

Federated Learning is a distributed learning paradigm with two key challenges that
differentiate it from traditional distributed optimization:(1) significant variability in terms of the …