Scaling Up: Distributed Machine Learning with Cooperation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Distributed Statistical Machine Learning in Adversarial Settings (2017, background): “Many efficient distributed machine learning algorithms [6, 13] and system implementations [24, 28, 10, 19] have been proposed and studied.”
Robust Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers (2018, background): “The design of distributed optimization algorithms has attracted significant recent research interests [1, 2, 3, 4, 5].”
A Survey of Methods for Scaling Up Inductive Algorithms (1999, background): “No matter what the run-time computational complexity of the algorithm, if exceeding the main memory limitation leads to virtual memory thrashing, the algorithm will not scale well (Provost and Hennessy, 1996).”
Distributed learning in wireless sensor networks (2005, background): “Inspired by the availability of increasingly large data sets, an active area of machine learning research focuses on “scaling up” existing learning algorithms to handle massive training databases; see, for example, [11], [17], [31], and [79] and references thereto and therein.”
Multiagent Inductive Learning: an Argumentation-based Approach (2010, methodology): “Similar to MALE, DRL (Provost & Hennessy, 1996) is a distributed rule learning algorithm based on finding rules locally and then sending them to the other agents for evaluation.”