# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 5 Discussion
### A Federated Learning Architecture  
Figure 7, inspired by one in ref. [10], shows the architecture that, considering what emerged from the present SLR, we envision as suitable for the deployment of EdgeML-based smart LSs. Before going into detail about the previous claim, hereafter a brief description of Figure 7 follows, moving from bottom to top.  
The first layer concerns smart IoT devices in the sense given in [106], that is, devices made up of one or more sensors directly connected, generally in Bluetooth or wireless mode, to an MCU, the latter equipped with memory and computing capacity, albeit to a limited extent. This layer can host an arbitrary number of smart devices among those listed in Table 1. The number of devices and their type depend on the addressed use case.  
The intermediate level in Figure 7 is composed of a certain number of nodes, each equipped with memory and computation capacity that is significantly greater than the level offered by the MUCs. Solutions based on the use of Raspberry Pi family hardware are widely reported in the literature [10]. These nodes can take charge of the training phase of any ML algorithm among those listed in Table 37, in addition to carrying out the task of sending the code of the corresponding inference algorithm to the smart devices connected to them. Figure 7 also highlights that \"horizontal\" collaborations are possible between the nodes of the intermediate level, useful, for example, to build the training model of the chosen ML algorithm, also drawing on data present on multiple nodes of the intermediate level.  
The Cloud layer is the network node where in a sense unlimited computing and data storage resources are concentrated; therefore, it is the natural location for the permanent storage of \"valuable\" data and their processing. Figure 7 ignores the application layer mentioned in Section 2.2 because it is not relevant at this stage of the study.  
An important feature of the architecture in Figure 7 is that it allows one to experiment with the use of federated ML algorithms. This implies the collaboration of the Cloud node with the nodes of the intermediate level, thus reducing the resources required at the edge layer, keeping at the same time the sensed data decentralized and hence secure. We envision that the adoption of this kind of architecture might be appropriate for the deployment of EdgeML-based smart LSs because of the findings in ref. [27]. In that study, [PERSON] et al. present an Edge Cloud architecture that leverages FL among layers by referring to the transportation domain as the use case. The authors proved that the adopted architecture  
Figure 7: A three-layer architecture of an EdgeML-based LS.  
delivers high-quality service to AIoT devices, while protecting the user privacy, so it overcomes the limitations of traditional Cloud computing ones.  
At the beginning of 2022, [PERSON] et al. [4] stated that the smart lighting technology was still in the proof-of-concept stage. The investigation of current trends coming from the manufacturers' side is outside the scope of the present SLR.  
Table 40 recaps the open issues discussed in this section. Each of them represents a research opportunity both for academia and industry.