# Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities
###### Abstract  
In the context of smart cities, the integration of artificial intelligence (AI) and the Internet of Things (IoT) has led to the proliferation of AIoT systems, which handle vast amounts of data to enhance urban infrastructure and services. However, the collaborative training of deep learning models within these systems encounters significant challenges, chiefly due to data privacy concerns and dealing with communication latency from large-scale IoT devices. To address these issues, multi-level split federated learning (multi-level SFL) has been proposed, merging the benefits of split learning (SL) and federated learning (FL). This framework introduces a novel multi-level aggregation architecture that reduces communication delays, enhances scalability, and addresses system and statistical heterogeneity inherent in large AIoT systems with non-IID data distributions. The architecture leverages the Message Queuing Telemetry Transport (MQTT) protocol to cluster IoT devices geographically and employs edge and fog computing layers for initial model parameter aggregation. Simulation experiments validate that the multi-level SFL outperforms traditional SFL by improving model accuracy and convergence speed in large-scale, non-IID environments. This paper delineates the proposed architecture, its workflow, and its advantages in enhancing the robustness and scalability of AIoT systems in smart cities while preserving data privacy. federated learning; split learning; split federated learning; artificial intelligent internet of things; edge computing +
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In recent years, the concept of distributed collaboration machine learning (DCML) has been proposed to solve the above challenges, including federated learning [5; 6; 7] and split learning [8; 9; 10]. Different from traditional centralized machine learning, DCML addresses data privacy challenges by collaborating with multiple IoT devices (clients) to train machine learning or deep learning models in collaboration with a central server, without sharing local data generated by individual IoT devices. In federated learning (FL), the global model is constructed by aggregating the model parameters trained by the local model on each client through the cloud server. In split learning (SL), the deep learning model is split into two parts: the first few layers are trained by the IoT device (client), and the bottom layer is calculated by the central server (cloud), which is mainly used to solve the problem of the limited computing resources of IoT devices. However, the performance of split learning decreases as the number of clients increases, and federated learning is not suitable for IoT devices with limited resources, so they all have limitations in large-scale AIoT systems. Therefore, split federated learning [11; 12; 13] is proposed to combine the advantages of SL and FL to make an AIoT system parallel in data and model training, which not only considers the problem of limited client resources but also reduces the influence of the number of clients on the performance of the model. Furthermore, it does not lose the protection of data privacy and the robustness of the model. Although split federated learning has become a new paradigm for future collaborative learning in AIoT systems, the performance and efficiency of model training are greatly reduced due to the large number of clients contained in large AIoT systems, the different transmission distances of different clients, and the insufficient stability of single cloud center server nodes. During the training process, there may be statistical heterogeneity and system heterogeneity among AIoT devices that have different computing and storage resources, and the generated data are not independent and identically distributed (non-IID). These factors all affect the performance of models trained in a split federated learning framework with only an end-cloud architecture.