Z Du, C Wu, T Yoshinaga, KLA Yau… - IEEE Open Journal of …, 2020 - ieeexplore.ieee.org
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 …
C He, M Annavaram… - Advances in Neural …, 2020 - proceedings.neurips.cc
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 …
Q Wu, X Chen, Z Zhou, J Zhang - IEEE Transactions on Mobile …, 2020 - ieeexplore.ieee.org
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 …