Edge computing is transforming the traditional approach to data processing and workload management by shifting computational tasks closer to the point of data generation. A network of decentralized nodes such as IoT devices, micro data centers and mobile edge infrastructure collaborate among themselves to find the cheapest paths for the workloads to process in federated edge environment. Unlike classical centralized cloud architecture, federated edge provides computational power at multiple autonomous domains and improves latencies reduction, fault tolerance, system resilience, and scalability. However, these benefits come at a price, as this paradigm brings a number of hurdles to the table regarding heterogeneity of devices, varying network characteristics, resource limitation, security risk, and cross collaboration with heterogeneous systems. This chapter discusses Edge Resource Federation (ERF), a multi-domain coordination framework that renders fast and seamless integration, workload balancing, as well as cooperative resource management of distributed edge resources. It discusses the foundation technical issues to federate hardware and software components at the edge including the heterogeneity, the need for real time decision making when network conditions change, and the security risks arising from the decentralized data processing at the edge environment. It also explores state of the art solutions like KubeEdge, StarlingX and ETSI MEC (Multiaccess Edge Computing) standard for orchestration and security enforcement in federated edge networks. Secondly, the chapter outlines best practices of operationalizing massive edge deployments including promulgating edge native Service Level Agreements (SLAs), powering the edge minimally by scheduling according to energy usage, and implementing zero trust security principles. The distribution strategy is used to ensure workload is present, reliable, secure and the compliance with geopolitical data sovereignty regulations. Finally, the chapter shows real world case study where federated edge computing has been used in the field of healthcare, autonomous systems and next generation telecommunications. The applications are illustrative examples of federated medical imaging models to aid stroke faster diagnosis, remote patient monitoring enabled with AI driven analytics at the edge, 5G network slicing for dynamic bandwidth allocation, digital twin technology to enable precision driven simulation in smart infrastructure. This chapter gives a complete view on how ERF is revolutionizing modern computing architectures by addressing both the challenge and the technological progress in federated edge computing. On the edge, federated learning, quantum-secure communication protocol, and federated edge computing will certainly play an increasing role in the development of the federated edge computing, which will facilitate the real time, privacy protecting, and even globally scalable distributed computations.