# Multi-Level Split Federated Learning for Large-Scale AIoT System Based on Smart Cities
###### Abstract
In addition, it has been found that model aggregation frequency can significantly affect federated learning performance [14]. This inspired us to propose a multi-level split federated learning (multi-level SFL) framework in which the large-scale client model parameters can be initially aggregated at the edge layer, fog layer, or higher levels, compensating for the scalability of traditional SFL in large AIoT systems. Since the parameters of the client can be preliminarily aggregated at multiple levels before being sent to the cloud server, the communication delay between the cloud server and the client is reduced, the processing speed of the central server is improved, and the global model can be trained by receiving the model parameters of the client in a wider range. The current hierarchical federated learning architecture is composed of client-edge-cloud system, which solves the communication efficiency problem of traditional cloud-based federated learning [15]. However, in the scenario of large AIoT systems based on smart cities, it is difficult for hierarchical FL to cover a larger number of IoT devices and be more widely distributed, so this architecture still limits the number of IoT devices that can be accessed. Compared to hierarchical FL, multi-level architectures can receive a wider range of IoT devices and adjust the number of layers of the architecture as the training task or the number of IoT devices changes, thereby enhancing the generalization of federated learning in AIoT systems. The multi-level aggregation architecture also reduces the single point of failure of the cloud server and resolves the problem of the model training being stuck because the client cannot upload the model information in time due to the long transmission distance. The addition of split learning balances the problem of system heterogeneity among clients and enhances the scalability of the system to incorporate more IoT devices for collaborative learning. Allocating only a portion of the network for training on the end devices can reduce the processing load compared to running a full network in multi-level FL. This enables resource-limited IoT devices to participate in collaborative training, enhancing the diversity of trainable tasks and reducing the impact of data silos in resource-limited devices. Compared with traditional split federated learning, our proposed framework can better solve the system and statistical heterogeneity and improve the scalability of large-scale AIoT systems. The main contributions of this paper are as follows:  
1. This paper proposes a multi-level split federated learning architecture based on IoT device location model information aggregation. The architecture reduces the communication delay between the client and the cloud server. Compared to hierarchical FL, multi-level SFL improves the scalability of the AIoT system through initial aggregation in multi-level edge nodes before the cloud server's aggregation. 2. The split learning algorithm is added to multi-level federated learning, which reduces the impact of system heterogeneity on client collaborative learning and the possibility of abandonment due to limited client computing resources. 3. We utilize the Message Queuing Telemetry Transport (MQTT) protocol to aggregate geographically located IoT devices by sending topics and assigning the nearest master server for split learning training. The client groups in each region communicate with the primary server through their respective local networks. 4. Simulation experiments on multi-level split federated learning using Docker verify that our proposed framework can effectively improve the model accuracy of collaborative training under large-scale clients. In addition, compared with traditional SFL, multi-level SFL in non-IID scenarios can converge faster and reduce the influence of non-IID data on model accuracy. The rest of this article is organized as follows. Section 2 reports some work related to the article.