FedKit: Enabling Cross-Platform Federated Learning for Android and iOS
This work addresses the problem of cross-platform federated learning deployment for researchers and practitioners, though it is incremental as it builds on existing FL concepts with platform-specific adaptations.
The authors tackled the challenge of implementing federated learning across Android and iOS platforms by developing FedKit, a system that enables model conversion, hardware-accelerated training, and cross-platform aggregation, and demonstrated its effectiveness in a real-world health data analysis use case on university campuses.
We present FedKit, a federated learning (FL) system tailored for cross-platform FL research on Android and iOS devices. FedKit pipelines cross-platform FL development by enabling model conversion, hardware-accelerated training, and cross-platform model aggregation. Our FL workflow supports flexible machine learning operations (MLOps) in production, facilitating continuous model delivery and training. We have deployed FedKit in a real-world use case for health data analysis on university campuses, demonstrating its effectiveness. FedKit is open-source at https://github.com/FedCampus/FedKit.