# Clustered Distributed Learning Exploiting Node Centrality and Residual Energy (CINE) in WSNs
## 2 Related Work  
In this section, we will discuss the recent works that are most relevant to our proposed approach. More specifically, We will discuss the distributed learning approaches, especially model-aggregation-based schemes in which clustering is exploited. In [22], the authors propose a FL scheme by combining FL and hierarchical clustering with separate clusters of devices according to the similarity of their local mode updates to the global joint model. After the separation, the clusters in the network are trained independently. The authors compared the performance of their proposed approach with the approach in which clustering is not exploited and observed that the model training in the proposed approach converged in fewer communication rounds. Similarly, in [23], the authors proposed a similarity aware FL scheme that can contribute high model accuracy for human-activity recognition applications. The scheme has a clustering FL framework that captures the relationship among the data of different nodes. Upon learning the cluster relationship, the nodes that converge more slowly can be dropped to speed up the convergence. The proposed scheme was evaluated on an NVIDIA edge testbed using human-activity recognition data collected from a total of 145 users. In [24], the authors develop a clustering-based FL framework that groups into multiple clusters. Each communication round of the framework consists of multiple cycles of meta-update to speed up the overall convergence. Experiments were conducted exploiting deep learning algorithms to show that the proposed framework achieves convergence significantly faster than the standard FL approach. Another clustering-based FL approach is proposed in [25] in which the geometric properties of the FL loss surface are considered to group the devices into clusters. Thus, each cluster has devices with jointly trainable data distributions. The proposed scheme is designed to preserve privacy and can handle numerous client devices that vary over time. Though many clustering-based schemes have been proposed in the literature to enhance FL, their main objective has been grouping devices based on either similarity in terms of data or in terms of model parameters. In our work, we developed a decentralized scheme that is based on grouping nodes such that a central node, also known as the cluster head, in the cluster has higher machine learning and communication capabilities than other nodes. The role of the central node is rotated to achieve fairness in the network. The chosen cluster head broadcasts its model parameters to the cluster members. The cluster members that received the model parameters aggregate the received model parameters with their own model parameters, as is done in FL. We discuss the recent works in which gossiping is combined with FL to reduce the consumption of energy and communication resources. In [16, 17], the authors introduce an integrated networking/learning approach for WSNs by combining FL with gossiping. The proposed approach enables the nodes to perform model aggregation locally instead of transmitting the model parameters to the central aggregation point for aggregation. Similarly, the authors in [18] develop a centrality aware gossiping protocol distributed learning in WSNs. The nodes exploit the centrality information to enhance the collaborative learning of the model. More specifically, the centrality information is exploited in the algorithm that is designed for identifying the nodes that are supposed to broadcast their model parameters in each communication round. It was observed that the proposed approach performs significantly better with the inclusion of the centrality information. Similarly, the authors in [20] propose a server-less consensus based learning approach for massive IoT networks. The proposed approach is assessed using data collected in an industrial IoT environment.