Cooperation of multiple devices to learn and make decisions based on their environment is especially valuable for Internet of Things (IoT) and other applications. Existing algorithms for cooperative learning often assume that all devices face the same set of decision choices, which is not often the case in networked system settings. This project, CoLeNe (Cooperative Learning in heterogeneous Networks), proposes to design and evaluate algorithms for multiple devices to cooperatively learn for decision making over a large set of choices in computer networks as the agents may face different sets of decisions. The developed algorithms allow devices to collaboratively explore the decision space and identify the best option from a large set faster. The project also applies these algorithms to the decision-making cases in networks and demonstrates their usefulness through the examples. In addition, the research effort is paired with educational and outreach initiatives that introduce students to the theory and practice of cooperative learning in networks. This proposal aims to develop cooperative online learning algorithms that are communication-efficient and robust to heterogeneity in computation, data, and privacy across agents. It consists of two research thrusts: (1) theoretical foundations and algorithms. The project will extend existing theoretical work on online learning to the settings of multiple heterogeneous agents with different sets of decision choices and privacy co