Robust collaborative learning by multi-agents

Document Type

Conference Proceeding

Publication Date

8-17-2015

Publication Title

2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2015 - Proceedings

First Page

183

Keywords

collaborative filtering, Collaborative learning, Distributed collaborative analytics, distributed filtering, distributed pattern learning

Last Page

187

Abstract

In this paper, we introduce a collaborative learning problem that is applicable in multi-agent data mining using heterogeneous computing resources in environments with limited control, resource failures, and communication bottlenecks. Specifically, we consider the scenario in which multiple agents collect noisy and overlapping information regarding an entity, such as a network attribute, which might correspond to multiple models. The agents are unable to share the entire information due to communication bottlenecks and other strategic issues; instead, the agents share their 'local estimate' about the entity. The objective is to obtain the best estimate of the true value of the entity based on the local estimates shared by the agents. First, we derive a centralized solution where the locally processed information from each agent is assumed available at a central node. Then, we develop a distributed solution to the problem that is suitable to environments with limited control, resource failures, and communication bottlenecks.

DOI

10.1109/CISDA.2015.7208646

ISBN

9781467375573

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