TY - JOUR

T1 - A Survey on Distributed Machine Learning

AU - Verbraeken, Joost

AU - Wolting, Matthijs

AU - Katzy, Jonathan

AU - Kloppenburg, Jeroen

AU - Verbelen, Tim

AU - Rellermeyer, Jan

N1 - Publisher Copyright: © 2020 ACM.

PY - 2020/3/20

Y1 - 2020/3/20

N2 - 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 leverage hardware acceleration. However, to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a substantial amount of training data is required. Although small machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across multiple machines, and turning the centralized into a distributed system. These distributed systems present new challenges: first and foremost, the efficient parallelization of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.

AB - 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 leverage hardware acceleration. However, to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a substantial amount of training data is required. Although small machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across multiple machines, and turning the centralized into a distributed system. These distributed systems present new challenges: first and foremost, the efficient parallelization of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.

KW - Distributed machine learning

KW - distributed systems

UR - http://www.scopus.com/inward/record.url?scp=85087906333&partnerID=8YFLogxK

U2 - 10.1145/3377454

DO - 10.1145/3377454

M3 - Review article

VL - 53

JO - ACM Computing Surveys (CSUR)

JF - ACM Computing Surveys (CSUR)

SN - 1557-7341

IS - 2

M1 - 3377454

ER -