Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Scalable training of large models (like BERT and GPT-3) requires careful optimization
rooted in model design, architecture, and system capabilities. From a system standpoint …

Communication is widely known as the primary bottleneck of federated learning, and
quantization of local model updates before uploading to the parameter server is an effective …

Federated learning is a powerful distributed learning scheme that allows numerous edge
devices to collaboratively train a model without sharing their data. However, training is …

Does Federated Learning (FL) work when both uplink and downlink communications have
errors? How much communication noise can FL handle and what is its impact on the …

Machine Learning (ML) has demonstrated great promise in various fields, eg, self-driving,
smart city, which are fundamentally altering the way individuals and organizations live, work …

Academic and industrial communities have been paying significant attention to the 6th
Generation (6G) wireless communication systems after the commercial deployment of 5G …

We propose TopoOpt, a novel direct-connect fabric for deep neural network (DNN) training
workloads. TopoOpt co-optimizes the distributed training process across three dimensions …

Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is
bandwidth-demanding and time-consuming. Frequent exchanges of node features …