Internet of things (IoT) is slowly permeating every aspect of our lives; however, we are far from having a truly intelligent IoT. Smart sensors generate massive amounts of data continuously; for instance, an autonomous vehicle is expected to generate about one gigabyte of data per second, but more often than not data is not systematically processed, stored, or analyzed for better inference. Many specialized machine learning (ML) algorithms have been developed to learn from sensor measurements, but these assume a centralized setting, where data is available at a central processor with powerful computation capabilities. This centralized approach assumes that the massive amount of sensor data is transmitted to a cloud center, which may not be feasible due to limitations of the devices and channels, not meet the stringent delay constraints of most applications, e.g., controlling an autonomous vehicle, or the privacy requirements of users. In the CONNECT project, our goal is to develop real edge intelligence by enabling edge nodes to make local decisions rapidly and reliably in a collaborative manner. This will be achieved by developing novel caching, distributed computing and networking methodologies to enable federated/ distributed learning taking into account the network dynamics and physical channel variations. The developed joint computing, caching and communication framework will then be applied to a hierarchical heterogeneous architecture for vehicular ad-hoc networks (VANETs). This will not only enable efficient and reliable learning across mobile nodes, but also improve the security and privacy of autonomous cars by limiting decision making to local neighborhood. Integration of caching, computing and networking will be demonstrated both through large-scale simulations, and on a small-scale implementation platform, consisting of two cars and a roadside unit at Koc University. This project is expected to enable many data intensive edge applications, from multimedia content streaming to participatory data collection in mobile networks, including autonomous cars, drones, mobile robots and mobile cellular users.