{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/14"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/8"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/16"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Flower is a framework for building federated learning systems where machine learning models are trained collaboratively across distributed clients without centralizing raw data.","skillfed_tags":["federated-learning","distributed-training","privacy-preserving"],"use_cases":["Train a model across hospitals' patient data without centralizing sensitive medical records.","Aggregate model updates from mobile devices in a federated learning pipeline using PyTorch or TensorFlow.","Implement custom federated learning strategies by extending Flower's Strategy class for research experiments.","Conduct federated analytics on distributed datasets without moving raw data to a central location.","Deploy a federated learning system with secure aggregation using Flower's cryptography integrations."],"what_it_does":"Flower is a framework for implementing federated learning systems\u2014distributed machine learning where model training happens across multiple clients that retain their data locally, with only model updates sent to a central server for aggregation. It abstracts away the complexity of coordinating training across heterogeneous devices and frameworks, allowing researchers and practitioners to focus on algorithm design rather than infrastructure. The framework is framework-agnostic and can work with PyTorch, TensorFlow, scikit-learn, JAX, XGBoost, and other ML libraries.\n\nFlower's core dependencies include gRPC and protobuf for client-server communication, cryptography libraries for secure aggregation, FastAPI and Uvicorn for server infrastructure, SQLAlchemy for state management, and rich/typer for CLI tooling. It is designed for customization and extensibility\u2014users can override strategies, define custom client logic, and integrate with their existing ML pipelines. The framework targets both research (with baseline implementations of published algorithms) and production use, supporting modern Python versions (3.11, 3.12, 3.13).","worth_installing":"Yes. Flower is production-stable (Development Status 5), actively maintained with recent releases, has no known vulnerabilities, and low install friction. Install it if you need to build federated learning systems or conduct distributed model training while preserving data privacy. The permissive Apache-2.0 license and framework-agnostic design make it suitable for research and commercial use."},"id":"flwr","links":{"html":"https://skillfed.io/packages/flwr","md":"https://skillfed.io/packages/flwr.md","pypi":"https://pypi.org/project/flwr/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-05","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"flwr","python_support":"supports_current","summary":"Flower: A Friendly Federated AI Framework"},"popularity":{"monthly_downloads":188759,"position":9938,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.33.0"}