Summary of Fedkit: Enabling Cross-platform Federated Learning For Android and Ios, by Sichang He et al.
FedKit: Enabling Cross-Platform Federated Learning for Android and iOS
by Sichang He, Beilong Tang, Boyan Zhang, Jiaoqi Shao, Xiaomin Ouyang, Daniel Nata Nugraha, Bing Luo
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary FedKit is a federated learning system designed for cross-platform research on Android and iOS devices. It enables model conversion, hardware-accelerated training, and cross-platform model aggregation, facilitating flexible machine learning operations (MLOps) in production. The system supports continuous model delivery and training, making it suitable for real-world use cases such as health data analysis. FedKit has been deployed on university campuses, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedKit is a special computer program that helps scientists work together to improve machine learning models on different kinds of devices like Android phones and iPhones. It makes it easier to convert models from one platform to another, train them faster using the device’s own processing power, and combine results from all platforms. This tool has real-world applications, such as analyzing health data on university campuses. It works well and is available for others to use. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
