Flower: A Framework-Agnostic Federated AI Framework
Flower is an open-source framework designed to facilitate the development of federated AI systems. These principles ensure that Flower can adapt to diverse use cases, integrate with multiple machine learning frameworks, and remain accessible for both research and production environments.
The framework originated from a research project at the University of Oxford and has since evolved into a collaborative tool used by researchers and engineers worldwide. Flower’s design prioritizes flexibility, allowing users to configure systems according to specific needs while maintaining the ability to extend and override components for advanced research applications.
Flower supports integration with a wide range of machine learning frameworks, including PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, CatBoost, and even raw NumPy for users who prefer manual gradient computation.
How It Works
Flower operates by enabling distributed training of machine learning models across multiple devices or servers without centralizing the underlying data. This approach ensures data privacy and compliance with regulations such as GDPR, as raw data never leaves the local devices where it resides.
The framework consists of several key components: a simulation engine for testing federated learning strategies, a deployment engine for real-world applications, and support for mobile and edge devices.
Clients can be deployed on various devices, including mobile phones, edge devices, and servers, making the framework adaptable to different infrastructure setups.
The communication between SuperLink and SuperNodes is facilitated through APIs, with recent updates transitioning from gRPC to HTTP for improved debugging and integration capabilities. This shift enhances the framework’s usability, particularly in production environments where HTTP-based tooling is more commonly utilized.
Use Cases
Flower is widely adopted across industries and regions, serving as a standard for federated AI deployments.
In healthcare, Flower has been integrated into secure research environments to enable federated learning across hospitals, ensuring patient data remains localized while still contributing to model training.
Flower also supports advanced use cases such as federated fine-tuning of large language models (LLMs), vision transformers, and even on-device training for mobile applications. The framework provides tutorials and examples for fine-tuning models like OpenAI’s Whisper and other LLMs, making it accessible for researchers and developers working with cutting-edge AI technologies.
The introduction of AgentApps allows for cross-federation communication, enabling agents to interact across different nodes within a network. This feature is particularly useful for developing decentralized AI systems that require coordination between multiple autonomous agents.
Framework Agnosticism
Flower’s compatibility with multiple machine learning frameworks is one of its defining features. Users can integrate Flower with PyTorch, TensorFlow, JAX, scikit-learn, and others, ensuring that the choice of framework does not limit the federated learning capabilities. This flexibility is further extended to edge and mobile devices through SDKs for Android (TFLite), iOS (CoreML), and native C++ implementations.
Simulation and Deployment Engines
The deployment engine, on the other hand, streamlines the transition from simulation to real-world applications, supporting scalable and production-grade federated AI systems.
AgentApps and Cross-Federation Communication
AgentApps represent a significant advancement in Flower’s functionality, enabling agents to run across federations and communicate with one another. For instance, an AgentApp on SuperLink can discover available SuperNodes, send JSON messages, and collect responses, facilitating complex workflows in decentralized AI environments.
Flower Baselines and Reproducibility
This resource allows researchers to build upon established work, evaluate new ideas, and contribute to the broader federated learning community. Baselines cover a wide range of algorithms, including FedAvg, FedProx, FedNova, and others, ensuring that users have access to well-tested and documented implementations.
Privacy and Security
Privacy is a cornerstone of Flower’s design, with features such as sample-level privacy using tools like Opacus and support for confidential remote compute. These capabilities ensure that federated learning processes adhere to strict privacy standards, making Flower suitable for regulated industries such as healthcare and finance. Additionally, Flower’s ISO 27001 certification underscores its commitment to maintaining high security standards for data protection.
Integration with Large Language Models (LLMs)
Flower supports the federated fine-tuning of LLMs, enabling decentralized training and collaboration on large-scale models.
Community and Ecosystem
Flower boasts a large and active community of researchers, engineers, and developers who contribute to its continuous improvement. The community engages through platforms like Slack, GitHub, and the Flower Hub, where users can discover, share, and publish AI applications. Flower Hub serves as a decentralized marketplace for federated AI apps, with features like app verification to ensure trust and compatibility within the ecosystem.
Tutorials and Documentation
Flower provides comprehensive tutorials and documentation to help users get started with federated learning. These resources cover fundamental concepts, framework-specific quickstarts, and advanced topics such as privacy, security, and scaling. The tutorials are designed to be accessible to both beginners and experienced practitioners, ensuring that users can implement federated learning solutions effectively.
Enterprise and Production Readiness
Flower is designed with enterprise-grade features to support large-scale deployments. Additionally, Flower offers forward-deployed engineering support to help organizations accelerate their AI projects and integrate federated learning into their workflows.