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FedKit: Enabling Cross-Platform Federated Learning for Android and iOS
Published 16 Feb 2024 in cs.LG and cs.NI | (2402.10464v1)
Abstract: 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.
- A. Farcas et al., “Demo abstract: A hardware prototype targeting federated learning with user mobility and device heterogeneity,” in IoTDI, 2023.
- C. He et al., “FedML: A research library and benchmark for federated machine learning,” 2020.
- D. Madrigal et al., “Project florida: Federated learning made easy,” Microsoft, Tech. Rep., Jul. 2023.
- A. Mathur et al., “On-device federated learning with flower,” 2021.
- A. J. Hall et al., “Syft 0.5: A platform for universally deployable structured transparency,” 2021.
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