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 work, 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.
For more information about our system, please read the following publications:
- FLaaS: Cross-App On-device Federated Learning in Mobile Environments
- FLaaS: Federated Learning as a Service
- Demo: FLaaS - Practical Federated Learning as a Service for Mobile Applications
- Demo: FLaaS - Enabling Practical Federated Learning on Mobile Environments
To get started, have a look at the following two main repositories: