Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …

With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …

Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

Data center clusters that run DNN training jobs are inherently heterogeneous. They have
GPUs and CPUs for computation and network bandwidth for distributed training. However …

The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

Despite existing work in machine learning inference serving, ease-of-use and cost efficiency
remain challenges at large scales. Developers must manually search through thousands of …

Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been
increasingly deployed to train deep learning models. These accelerators exhibit …

Large language models (LLMs) power a new generation of interactive AI applications
exemplified by ChatGPT. The interactive nature of these applications demands low latency …

With widespread advances in machine learning, a number of large enterprises are
beginning to incorporate machine learning models across a number of products. These …

We present ByteScheduler, a generic communication scheduler for distributed DNN training
acceleration. ByteScheduler is based on our principled analysis that partitioning and …