Efficient distributed training is a principal driver of recent advances ...
Federated learning is an emerging machine learning paradigm that enables...
With the application of machine learning to security-critical and sensit...
The advent of switches with programmable dataplanes has enabled the rapi...
Current serverless offerings give users a limited degree of flexibility ...
The learning rate (LR) schedule is one of the most important hyper-param...
It is becoming increasingly popular for distributed systems to exploit
n...
Compressed communication, in the form of sparsification or quantization ...
The adoption of very low latency persistent memory modules (PMMs) upends...
Optimization acceleration techniques such as momentum play a key role in...
Due to their hunger for big data, modern deep learning models are traine...
The conventional wisdom is that a software-defined network (SDN) operate...
Training complex machine learning models in parallel is an increasingly
...
Consensus protocols are the foundation for building fault-tolerant,
dist...
The emergence of programmable switches has sparked a significant amount ...
Software-Defined-eXchanges (SDXes) promise to tackle the timely quest of...
By introducing programmability, automated verification, and innovative
d...