A survey on distributed machine learning
The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …
growth has been fueled by advances in machine learning techniques and the ability to …
From distributed machine learning to federated learning: A survey
In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …
users, various regions or organizations. Because of laws or regulations, the distributed data …
Deep & cross network for ad click predictions
Feature engineering has been the key to the success of many prediction models. However,
the process is nontrivial and often requires manual feature engineering or exhaustive …
the process is nontrivial and often requires manual feature engineering or exhaustive …
Scaling distributed machine learning with the parameter server
We propose a parameter server framework for distributed machine learning problems. Both
data and workloads are distributed over worker nodes, while the server nodes maintain …
data and workloads are distributed over worker nodes, while the server nodes maintain …
Communication efficient distributed machine learning with the parameter server
This paper describes a third-generation parameter server framework for distributed machine
learning. This framework offers two relaxations to balance system performance and …
learning. This framework offers two relaxations to balance system performance and …
[PDF][PDF] A reliable effective terascale linear learning system
We present a system and a set of techniques for learning linear predictors with convex
losses on terascale data sets, with trillions of features, 1 billions of training examples and …
losses on terascale data sets, with trillions of features, 1 billions of training examples and …
A blockchain-based decentralized machine learning framework for collaborative intrusion detection within UAVs
UAVs have numerous emerging applications in various domains of life. However, it is
extremely challenging to gain the required level of public acceptance of UAVs without …
extremely challenging to gain the required level of public acceptance of UAVs without …
[PDF][PDF] Scaling distributed machine learning with system and algorithm co-design
Due to the rapid growth of data and the ever increasing model complexity, which often
manifests itself in the large number of model parameters, today, many important machine …
manifests itself in the large number of model parameters, today, many important machine …
Performance modeling of distributed deep neural networks
During the past decade, machine learning has become extremely popular and can be found
in many aspects of our every day life. Nowayadays with explosion of data while rapid growth …
in many aspects of our every day life. Nowayadays with explosion of data while rapid growth …
[PDF][PDF] Distributed delayed proximal gradient methods
We analyze distributed optimization algorithms where parts of data and variables are
distributed over several machines and synchronization occurs asynchronously. We prove …
distributed over several machines and synchronization occurs asynchronously. We prove …