###### Abstract
First, the centralized solution needs to be deployed on a centralized controller or cloud servers. The centralized solution requires transmission of data from end devices to a centralized controller or cloud server [12], which leads to high transmission, computing and signaling costs [13]. Secondly, the transmission of data and the issuance of control commands in centralized solution cause additional delays. Coupled with the calculation time of the algorithm, many scenarios and services, such as URLLC [14], cannot accept the high time cost of a centralized solution. Moreover, 5G and B5G network topology is more complex and provides different types of services to UE, which puts forward higher requirements to D-TDD technology. According to the paper [15], 5G and B5G network should be concise and expandable, and centralized solutions reduce the expandability of network. Coping with this issue, a distributed solution becomes an option. In contrast with centralized schemes, distributed solutions can improve computation efficiency by parallel execution at distributed locations. Considering that the wireless communication network is a natural distributed system, a decentralized solution can be distributed deployed in each BS. Combined with MEC technology, data collection and related computing tasks are performed by MEC servers. This reduces the control delay and signaling overhead. In the paper [16], the system-level simulation of centralized and decentralized dynamic TDD resource allocation schemes were conducted. The results show that distributed D-TDD improves resource utilization and reduces latency. In recent years, MARL, as a new pattern of the decentralized solution, has attracted the attention of many researchers [17]. MARL expends reinforcement learning to the field of the multi-agent environment. The task of learning in a multi-agent environment is more complicated than that in a single-agent environment, because agent interacts with environment and other agents at the same time, which increases the non-stationarity of the learning environment. In order to improve the performance of learning in a multi-agent environment, researchers generally use four methods: (1) Extended reinforcement learning: extend RL to multi-agent environment directly; (2) Learning communication: agent communicates with each other to complete learning task; (3) Learning cooperation: agent learns to cooperate without communication; (4) Agents modeling agents: agent reason about the behaviors of other agents by predicting other agents. In addition to MARL, federated learning (FL) is also seen as a distributed learning solution. FL is a machine learning method in which distributed learning nodes (e.g., mobile devices) collaboratively train a model under the orchestration of a central controller (e.g., service provider), while keeping the training data decentralized [18]. According to paper [19], FL can be divided into three categories: (1) Horizontal Federated Learning: datasets in distributed nodes share the same feature space but are different in samples; (2) Vertical Federated Learning: datasets in distributed nodes differ in feature space but share the same feature space; (3) Federated Transfer Learning: datasets in distributed nodes are different in samples and feature space. Decentralized training data can mitigate the privacy and overhead issues of traditional centralized machine learning. Thus, some researchers give rise to the idea of federated reinforcement learning (FRL) which can be considered as an integration of FL and RL [20]. According to paper [21], MARL research is similar with FRL research [22], but FRL has advantages of high privacy protection and wide application scenarios. Therefore, the paper [23] proposed a federated RL-based channel resource allocation framework for 5G/B5G networks, and suggested collaborating learning estimates for faster learning convergence.