This work proposes a reliable leakage detection analysis for water distribution networks (WDNs) by combining efficient and emergent machine learning techniques. In this study case, we analyze pressure and flow measurements from pumps in Stockholm, Sweden, where we consider a residential district metered area of the WDN. Our solution aims at detecting leakage in WDNs using a prototype-based model (PBM) while preserving data privacy by proposing a federated learning approach. The machine learning strategies we adopt have low complexity, and the numerical experiments show the potential of using federated prototype-based techniques for leakage detection on monitored WDNs. Specifically, our experiments show that the proposed learning method can obtain higher detection rates at each pumping station than the conventional centralized approach, e.g. improvements of purity rates up to 7.6% in one of the pumping stations, which increased the minimum values from 92.13%, obtained through centralized learning, to 99.11%, obtained via federated learning.