Many modern engineering applications require distributed problem-solving in which multiple nodes in a network cooperate to achieve a common optimization objective. Numerous distributed optimization and learning algorithms rely on consensus-based distributed coordination to ensure network-wide agreement via iterative local interactions. In these algorithms, the accuracy, frequency, and reliability of information exchange among network nodes directly influence system performance, including convergence behavior, training delay, and model accuracy. This tutorial article adopts a bottom-up approach to present the fundamentals of consensus averaging and distributed optimization methods, and highlights the impact of network connectivity, information flow, and communication uncertainty on network cooperation and optimization performance. Building on these theoretical insights, we discuss key design principles for communication-constrained distributed learning across diverse network topologies and review common approaches to communication-efficient learning, particularly in wireless networks. Despite substantial progress, further efforts are needed to bridge the gap between theory and practice and fully realize the potential of cooperative and networked intelligence.

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