Indoor localisation is a well-researched topic and it is a challenge to improve the accuracy of existing techniques. In recent years, edge computing and federated learning have opened up new possibilities and challenges for indoor localisation. This thesis presents a federated implementation for spatial mapping of the network based on the RSSI signal between nodes. The proposed approach decentralises indoor localisation and produces multiple coordinate maps of the nodes in a network. The coordinate maps and edge data are from multiple node perspectives, which can improve the aggregation of the coordinates into a single map. The algorithm is tested in a BLE network and is compared with other current methods. In addition, a simulator is built that serves as a proof of concept for the actual implementation of the algorithm. The results of the simulator prove that the mathematical aspects are correct and that the algorithm consistently reproduces the same structure with a stable signal. Time-series analysis of RSSI measurements along with environmental factors unveils periodic noise components such as humidity and human presence, along with the notable influence of node placement and ambient noise. Consequently, the conventional RSSI-to-distance model proves inadequate in adapting to such noise sources. The spatial mapping algorithm helps in creating a 3D structure in a federated manner and provides a framework and location reference for future research that employs adaptable machine learning models. However, its localisation accuracy is still limited in comparison to other preexisting techniques.