Gradient compression is a widely-established remedy to tackle the communication
bottleneck in distributed training of large deep neural networks (DNNs). Under the error …

Gradient compression (GC) is a promising approach to addressing the communication
bottleneck in distributed deep learning (DDL). It saves the communication time, but also …

Rapid advances in machine learning necessitate significant computing power and memory
for training, which is accessible only to large corporations today. Small-scale players like …

Distributed Mean Estimation (DME), in which $ n $ clients communicate vectors to a
parameter server that estimates their average, is a fundamental building block in …

Due to the explosion in the size of the training datasets, distributed learning has received
growing interest in recent years. One of the major bottlenecks is the large communication …

Gradient compression (GC) is a promising approach to addressing the communication
bottleneck in distributed deep learning (DDL). However, it is challenging to find the optimal …

This thesis focuses on personalized learning in education, a promising and effective means
of learning where the instructions, educational materials, learning paths, analytics, and …

Distributed Mean Estimation (DME) is a fundamental building block in communication
efficient federated learning. In DME, clients communicate their lossily compressed gradients …

Deep neural networks (DNNs) have achieved unparalleled performance in numerous fields,
including computer vision, natural language processing, and recommendation systems …