Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …

Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Federated Learning (FL) has emerged as a promising technique for edge devices to
collaboratively learn a shared prediction model, while keeping their training data on the …

During typical gradient-based training of deep neural networks, all of the model's
parameters are updated at each iteration. Recent work has shown that it is possible to …

Huge scale machine learning problems are nowadays tackled by distributed optimization
algorithms, ie algorithms that leverage the compute power of many devices for training. The …

Federated learning (FL) allows model training from local data collected by edge/mobile
devices while preserving data privacy, which has wide applicability to image and vision …

Federated Learning is a machine learning scheme in which a shared prediction model can
be collaboratively learned by a number of distributed nodes using their locally stored data. It …

Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed
training. The scheme can reach a linear speedup with respect to the number of workers, but …

Large-scale distributed training requires significant communication bandwidth for gradient
exchange that limits the scalability of multi-node training, and requires expensive high …