# FedKit: Enabling Cross-Platform Federated Learning for Android and iOS

arXiv: https://arxiv.org/abs/2402.10464 · HTML: https://mlsanity.com/paper/2402.10464/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 5 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [On-device Federated Learning with Flower](https://mlsanity.com/paper/2104.03042/) (2021)
- [Syft 0.5: A Platform for Universally Deployable Structured Transparency](https://mlsanity.com/paper/2104.12385/) (2021)
- [Project Florida: Federated Learning Made Easy](https://mlsanity.com/paper/2307.11899/) (2023)

## Built on by (most cited first, 2 of 2)

- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics](https://mlsanity.com/paper/2409.00327/) (2024)

## Abstract (arXiv)

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.