Serverless computing is gaining popularity for machine learning (ML) serving workload due
to its autonomous resource scaling, easy to use and pay-per-use cost model. Existing …

Federated Learning (FL) is a machine learning paradigm that enables the training of a
shared global model across distributed clients while keeping the training data local. While …

Gradient accumulation (GA) is a commonly adopted technique for addressing the GPU
memory shortage problem in model training. It reduces memory consumption at the cost of …

The increasing demand for computational power in big data and machine learning has
driven the development of distributed training methodologies. Among these, peer-to-peer …

The field of distributed machine learning (ML) faces increasing demands for scalable and
cost-effective training solutions, particularly in the context of large, complex models …

The advent of serverless computing has ushered in notable advancements in distributed
machine learning, particularly within parameter server-based architectures. Yet, the …

Serverless computing has received growing interest in recent years for supporting large-
scale machine learning tasks. However, training a machine learning model in a serverless …

Distributed Machine Learning refers to the practice of training a model on multiple
computers or devices that can be called nodes. Additionally, serverless computing is a new …

This study examines the application of pre-trained machine learning models on serverless
cloud computing platforms, specifically comparing three serverless services offered by …

Serverless computing simplifies cloud programming by managing infrastructure and
providing basic primitives for building distributed components. However, these efforts tend to …