Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

The privacy-preserving federated learning for vertically partitioned (VP) data has shown
promising results as the solution of the emerging multiparty joint modeling application, in …

Due to the need to store the intermediate activations for back-propagation, end-to-end (E2E)
training of deep networks usually suffers from high GPUs memory footprint. This paper aims …

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the
future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to …

A commonly cited inefficiency of neural network training by back-propagation is the update
locking problem: each layer must wait for the signal to propagate through the network before …

Reliable estimation of the remaining useful life (RUL) of complex engineered systems plays
a vital role in avoiding undue maintenance situations while guaranteeing system safety …

Recently, deep learning has achieved great success in visual tracking tasks, particularly in
single-object tracking. This paper provides a comprehensive review of state-of-the-art single …

Greedy layer-wise or module-wise training of neural networks is compelling in constrained
and on-device settings where memory is limited, as it circumvents a number of problems of …

Traditional deep learning relies on end-to-end backpropagation for training, but it suffers
from drawbacks such as high memory consumption and not aligning with biological neural …

Fine-tuning is an increasingly common technique that leverages transfer learning to
dramatically expedite the training of huge, high-quality models. Critically, fine-tuning holds …