Platform independence in federated learning refers to the capability of machine learning systems to operate seamlessly across various hardware and operating system environments. This ensures that the federated learning process can function effectively on various devices with different operating systems such as Windows, Linux, Mac OS, iOS, and Android without requiring platform-specific modifications. By achieving platform independence, federated learning frameworks enable efficient data analysis and model training across heterogeneous edge devices, enhancing scalability and flexibility in distributed machine learning scenarios.
Platform Independence in Flower
Flower is interoperable with different operating systems and hardware platforms to work well in heterogeneous edge device environments.