The problem of learning simultaneously several related tasks has received considerable attention in several domains, especially in machine learning with the so-called multitask learning problem or learning to learn problem [1], [2]. Multitask learning is an approach to inductive transfer learning (using what is learned for one problem to assist in another problem) and helps improve generalization performance relative to learning each task separately by using the domain information contained in the training signals of related tasks as an inductive bias. Several strategies have been derived within this community under the assumption that all data are available beforehand at a fusion center. However, recent years have witnessed an increasing ability to collect data in a distributed and streaming manner. This requires the design of new strategies for learning jointly multiple tasks from streaming data over distributed (or networked) systems. This article provides an overview of multitask strategies for learning and adaptation over networks. The working hypothesis for these strategies is that agents are allowed to cooperate with each other in order to learn distinct, though related tasks. The article shows how cooperation steers the network limiting point and how different cooperation rules allow to promote different task relatedness models. It also explains how and when cooperation over multitask networks outperforms non-cooperative strategies.

中文翻译：

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图上的多任务学习

同时学习多个相关任务的问题在多个领域受到了相当大的关注，特别是在机器学习中，所谓的多任务学习问题或学习学习问题[1]，[2]。多任务学习是一种归纳迁移学习的方法（使用针对一个问题所学的知识来辅助另一个问题），并通过使用相关任务的训练信号中包含的领域信息作为归纳来帮助提高相对于单独学习每个任务的泛化性能偏见。在假设所有数据都可在融合中心事先获得的情况下，在该社区内衍生出多种策略。然而，近年来，以分布式和流式方式收集数据的能力不断提高。这需要设计新的策略，以便从分布式（或网络）系统上的流数据中联合学习多个任务。本文概述了网络学习和适应的多任务策略。这些策略的工作假设是允许代理相互合作以学习不同但相关的任务。文章展示了合作如何引导网络限制点以及不同的合作规则如何促进不同的任务相关性模型。它还解释了多任务网络上的合作如何以及何时优于非合作策略。这些策略的工作假设是允许代理相互合作以学习不同但相关的任务。文章展示了合作如何引导网络限制点以及不同的合作规则如何促进不同的任务相关性模型。它还解释了多任务网络上的合作如何以及何时优于非合作策略。这些策略的工作假设是允许代理相互合作以学习不同但相关的任务。文章展示了合作如何引导网络限制点以及不同的合作规则如何促进不同的任务相关性模型。它还解释了多任务网络上的合作如何以及何时优于非合作策略。