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A fast, modular Python framework released by Apple for privacy-preserving federated learning (PFL) simulation. Integrates with TensorFlow, PyTorch, and classical ML, and offers high-speed distributed simulation (7–72× faster than alternatives).

A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language. Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case. Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. Many components can be extended and overridden to build new state-of-the-art systems. Different machine learning frameworks have different strengths. ...

...It helps developers to launch complex model training, deployment, and federated learning anywhere on decentralized GPUs, multi-clouds, edge servers, and smartphones, easily, economically, and securely. Highly integrated with TensorOpera open source library, TensorOpera AI provides holistic support of three interconnected AI infrastructure layers: user-friendly MLOps, a well-managed scheduler, and high-performance ML libraries for running any AI jobs across GPU Clouds. A typical workflow is shown in the figure above. When a developer wants to run a pre-built job in Studio or Job Store, TensorOperaLaunch swiftly pairs AI jobs with the most economical GPU resources, and auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management.