This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions while offering the potential for improved privacy, increased resilience, and conserved resources. A longer version of this manuscript, with examples and illustrative applications, is available at https://arxiv.org/abs/2210.13767 .

中文翻译：

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网络信号和信息处理：多智能体系统学习

本文回顾了网络信号和信息处理 (SIP) 方面的重大进展，这些进展在过去 25 年中将决策和推理、优化、控制和学习扩展到日益普遍的分布式代理环境中。当这些相互作用的主体合作时，局部决策和行动就会出现新的集体行为。此外，重要的是，理论和应用表明，网络代理通过合作和共享，能够匹配云或联合解决方案的性能，同时提供改善隐私、增强弹性和节省资源的潜力。该手稿的较长版本（包含示例和说明性应用）可在https://arxiv.org/abs/2210.13767 。