## 2 Related Work  
In this section, we summarize the latest research related to our proposed algorithm. The International Data Corporation (IDC) predicts that spending within the IoT ecosystem will exceed USD 1 trillion in 2060, with an expected compound annual growth rate (CAGR) of 10.4% from 2023 to 2027 [14]. One of the key elements determining the price of mobile computing in FL is communication overheads. Therefore, a major concern when implementing joint learning for IoT and mobile computing scenarios is how to lower the computation, storage, and communication costs of joint learning privacy protection approaches and how to improve the efficiency of joint learning [15]. In order to reduce the cost of the IoT ecosystem, the key issue is how to optimize the task computation strategy based on the specific user requirements of mobile devices. Based on the process of task allocation, we can categorize most of the existing research on edge cloud computing scheduling problems into two groups. In one category, we need to consider the decision problem for joint request offloading and resource scheduling (JRORS) before task execution, and in the other category, we need to consider the transmission power allocation problem (PA) during task communication. The rational allocation of computational resources prior to the start of a task, in order to achieve optimal performance or efficiency during execution, is the focus of JRORS. This involves the task scheduling arrangement, resource allocation, offloading strategy, etc. [PERSON] and [PERSON] [16] integrated the problems of co-optimizing the task offloading strategy, transmission capacity of mobile users, and resource allocation of edge servers into two separate problems of joint task offloading (TO) and resource allocation (RA), which they solved using convex and quasi-convex optimization techniques. However, making the entire system bandwidth available to a mobile device to transmit data may lead to network congestion and increase the energy consumption of the mobile device. [PERSON] and [PERSON] [17] constructed a data placement model that dynamically allocates newly generated datasets to appropriate data centers and removed exhausted datasets during workflow execution. [PERSON] [18] proposed a greedy staged offloading algorithm to solve the problem of task offloading. Although Odessa is fast, its offloading strategy is not optimal. [PERSON] [19] developed a simple architecture for offloading information-centric IoT applications based on task classification and computation functions. However, the architecture does not consider communication latency. [PERSON] and [PERSON] [20] provided a task offloading approach using power as a constraint, emphasizing the energy consumption and measurement latency factors in the optimization problem. [PERSON] et al. [21] proposed an optimal routing algorithm that determines the optimal route by modifying [PERSON]'s algorithm under real-time dynamic traffic flow conditions, allowing the users to interactively determine the optimal path and identify destinations efficiently. [PERSON] et al. [22] proposed a method for allocating resources in wireless networks using the whale optimization algorithm (WOA) and improving it as a binary version based on specific scenarios. [PERSON] et al. [23] proposed a modified particle swarm optimization algorithm (MPSO) for task scheduling, in order to achieve the goal of shortening the completion time of a task in cloud computing. [PERSON] et al. [24] and [PERSON] et al. [25] studied the joint computation offloading and resource scheduling (RS) problem; however, they only considered a base station (BS), to accomplish the computational tasks in IoT systems. During the task of carrying out the communication process, PA mainly solves the problem of how to allocate the transmission power appropriately to optimize the communication quality, energy consumption, and other factors during the communication process.