With the widespread use of distributed machine learning (DML), many IT companies have
established networks dedicated to DML. Different communication architectures of DML have …

Federated learning can collaboratively train a global model without gathering clients' private
data. Many works focus on reducing communication cost by designing kinds of client …

The link speed in production datacenters is growing fast, from 1 Gbps to 40 Gbps or even
100 Gbps. However, the buffer size of commodity switches increases slowly, eg, from 4 MB …

With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

As the scale of distributed training increases, it brings huge communication overhead in
clusters. Some works try to reduce the communication cost through gradient compression or …

Deep learning's widespread adoption in various fields has made distributed training across
multiple computing nodes essential. However, frequent communication between nodes can …

In recent years, federated learning has attracted more and more attention as it could
collaboratively train a global model without gathering the users' raw data. It has brought …

As an edge intelligence algorithm for multi-device collaborative training, federated learning
(FL) can protect data privacy but increase the computing load of wireless devices. In …

Deployment of distributed machine learning at the edge is conducive to reducing latency
and protecting privacy associated with transmitting data back to the cloud. Nonetheless, as …

In-network computing via smart networking devices is a recent trend for modern datacenter
networks. State-of-the-art switches with near line rate computing and aggregation …