  Federated Learning (FL) is emerging as a promising technology to build
machine learning models in a decentralized, privacy-preserving fashion. Indeed,
FL enables local training on user devices, avoiding user data to be transferred
to centralized servers, and can be enhanced with differential privacy
mechanisms. Although FL has been recently deployed in real systems, the
possibility of collaborative modeling across different 3rd-party applications
has not yet been explored. In this paper, we tackle this problem and present
Federated Learning as a Service (FLaaS), a system enabling different scenarios
of 3rd-party application collaborative model building and addressing the
consequent challenges of permission and privacy management, usability, and
hierarchical model training. FLaaS can be deployed in different operational
environments. As a proof of concept, we implement it on a mobile phone setting
and discuss practical implications of results on simulated and real devices
with respect to on-device training CPU cost, memory footprint and power
consumed per FL model round. Therefore, we demonstrate FLaaS's feasibility in
building unique or joint FL models across applications for image object
detection in a few hours, across 100 devices.
