• federated learning
• clustering
• kmeans
• fuzzy cmeans
• dbscan

My thesis work is in the federated learning scenario, where different devices, within the same network, contain different information. Therefore, in this scenario the need arises to implement a clustering model that does not involve the sharing of private information, but at the same time achieves the same results of the centralized version, where the server is aware of all the data.
The purpose of my thesis is therefore to define and implement federated versions of some of the most popular clustering algorithms currently available: k-means, fuzzy c-means and dbscan. The versions I have defined have been tested on horizontally and vertically partitioned datasets in order to simulate a federated environment.