## 4 Reinforcement Learning for IoT Networking Management
### Federated Learning Model for RPL Management  
This section briefly summarizes how the components of federated learning (FL)-based solutions in the IoT environment are formulated. This section highlights the key FL concepts and visions of using FL in IoT networks. FL has the potential to further enhance current IoT systems. It is a particularly attractive solution to build a distributed IoT system due to growing privacy leakage concerns. FL in IoT networks is composed of two primary components: the data clients, such as IoT devices, and an aggregation server acting as either a base station or an access point. FL allows the IoT devices and a server to train a global model while keeping the raw data in the devices. In FL-based training, each IoT device learns and trains a model using its local dataset. This locally trained model in FL is referred to as a _local model_. After the training, the devices transmit their local model to the server node or base station and then aggregate to create a shared model, which is referred to as a _global model_. This FRL-empowered mechanism can be introduced for LLN optimization. The goal of the FL mechanism is to train a global model. To generate an FL route update model, the IoT nodes can exploit the FL algorithm by which the participating child nodes collaboratively learn a shared model while keeping all the training data locally. Thus, FL is a distributed collaborative approach of IoT devices for data training with a central server node. The general FL process includes the following key steps. (1) Initialization:The child nodes first set up a learning parameter, such as rank measurement within the network hierarchy. The child nodes can share their rank values with neighboring nodes through the use of a DIO control packet. (2) DistributedLocal Training: The nodes with a similar rank hierarchy form a learning group, and one of the nodes in the group assumes the role of the learning server node, responsible for coordinating the training process. The learning agent, acting as the server node, initiates the training by updating the learning model using a locally learned model from child nodes. Each child node in a group obtains its value function using its own local data, thereby creating a local model. (3) ModelAggregation: After completing local training, the local models are aggregated to create a global model. At this stage, each device has undergone local training using its own local dataset. Each node sends its local model to the server node. After collecting all model updates from local IoT devices, the server aggregates them to calculate the global model. One of the main challenges of model aggregation is the need for efficient communication between the learners and the server node. Transmitting the entire local model from each node to the server node can be impractical due to bandwidth constraints. The learning devices exchange only model parameter updates such as Q-values. The frequency of communication between child nodes and the server node depends on the FL application and network conditions. It can be periodic, event-driven, or adaptive based on factors such as data availability or resource constraints. In addition, synchronization in terms of the timing of model updates is a crucial aspect of FL systems. Inconsistency in updating the local learning models may lead to convergence issues. Convergence criteria can be based on the change in global model parameters. After federated averaging, the server node uses this aggregated value to obtain its next value function. The learning agent updates the learning model using a locally learned model from other child nodes with the same wireless environment. Model aggregation in this scenario operates iteratively, with multiple rounds of local training and aggregation. In each round, nodes update their local models based on the global model, and these updates are subsequently aggregated.