# Edge Cloud Computing and Federated-Split Learning in Internet of Things
###### Abstract  
The wide deployment of the Internet of Things (IoT) necessitates new machine learning (ML) methods and distributed computing paradigms to enable various ML-based IoT applications to effectively process huge amounts of data. Federated Learning (FL) is a new collaborative learning method that allows multiple data owners to cooperate in ML model training without exposing private data. Split Learning (SL) is an emerging collaborative learning method that splits an ML model into multiple portions that are then trained collaboratively by different entities. FL and SL each have unique advantages and limitations and may complement each other in facilitating effective collaborative learning in an IoT environment. On the other hand, the rapid development of edge cloud computing technologies enables a distributed computing platform in IoT upon which FL and SL frameworks can be deployed. Therefore, the deployment of FL and SL in an edge cloud platform in an IoT environment has become an active area of research, attracting interest from both academia and industry. This Special Issue aims to present the latest research advances in this interdisciplinary field of edge cloud computing and federated-split learning. This Special Issue includes twelve research articles addressing various aspects of edge cloud computing and federated-split learning, including technologies for improving the performance and efficiency of both FL and SL in edge cloud computing environments, mechanisms for protecting data privacy and enhancing system security in FL and SL frameworks, and ways to exploit FL-/SL-based ML methods and edge cloud computing technologies in order to support various IoT applications. The constrained computing and communication resources available in IoT constitute the main challenge to high-performance federated and split learning. Therefore, technologies for computation and communication efficiency play a crucial role in the effective deployment of FL/SL frameworks in IoT. In [1], [PERSON] et al. investigate the resource allocation problem in virtualization-based edge cloud computing systems in order to maximize the efficiency of the FL process. The authors consider factors such as computational and network capabilities, the complexity of datasets, and the specific characteristics of the FL workflow. They explore two scenarios: (i) running FL over a finite number of nodes and (ii) hosting multiple parallel FL workflows on the same set of nodes. The research findings indicate that the default configurations of state-of-the-art cloud orchestrators are sub-optimal when orchestrating FL workflows, demonstrating that different libraries and ML models exhibit diverse computational footprints. Building upon these insights, the authors discuss methods to mitigate computational interference and enhance the overall performance of the FL pipeline's execution. Task scheduling is another key technology that has been explored to enhance FL efficiency and performance. In [2], [PERSON] et al. study two problems of task scheduling for FL in edge computing: (1) transmission power allocation (PA) and (2) the dual decision-making problems of joint request offloading and computational resource scheduling (JRORS). The authors propose an adaptive greedy dingo optimization algorithm (AGDOA) based on greedy policies and parameter adaptation in order to solve the PA problem. Theyalso construct a binary salp swarm algorithm (BSSA) that introduces binary coding to solve the discrete JRORS problem. Their simulation results verify that the proposed algorithms improve FL convergence speed, shorten the system response time, and reduce energy consumption. Future networking technologies such as the 6G network may greatly enhance edge-based IoT; however, they also introduce diverse and heterogeneous devices that present new challenges for FL and SL.