Federated Learning Frameworks

FATE is an open-source federated learning platform that enables multiple organizations to collaboratively train machine learning models without exposing raw data to any party. It provides a complete framework for private data collaboration, allowing participants to jointly compute on sensitive information while maintaining data privacy and security guarantees through secure multi-party computation protocols. The platform distinguishes itself through its comprehensive infrastructure management capabilities, supporting automated deployment of multi-party clusters using Ansible-driven provisioning and cloud-native technologies like containers and Kubernetes. FATE includes a DAG-based pipeline scheduler for orchestrating federated tasks, an Eggroll distributed compute engine for distributed data processing, and a federated model serving proxy for routing inference requests with privacy-preserving transformations. The system implements intersection-safe aggregation protocols and a party-role-based topology that assigns participants specific roles (guest, host, arbiter) to define data access and computation permissions. Beyond core training and serving, FATE offers capabilities for deploying standalone instances for local development, running on ARM architecture, and managing federated infrastructure through release artifacts and Docker containers. The platform also provides visualization tools for exploring model behavior and performance.

FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.

SecretFlow is a privacy computing framework and platform designed for secure multi-party computation, federated learning, and privacy-preserving data analysis across independent nodes. It provides a management system to coordinate secure workloads and cryptographic tasks across a distributed cluster. The project enables joint data analysis and machine learning on partitioned datasets using cryptographic protocols. It allows for the training of models and the execution of analytical queries across multiple parties without exposing raw source information to any single participant. The framework covers a broad surface of privacy-preserving capabilities, including secure distributed analytics, encrypted data processing, and distributed model development. It incorporates orchestration tools for managing private workflows and coordinating the sequence of computation steps across isolated environments.

Flower is a federated learning framework and distributed machine learning orchestrator designed to train models across decentralized devices. It functions as a privacy-preserving toolkit that enables model training and data analysis on local hardware, ensuring raw data remains on the device while contributing to a synchronized global model. The system employs an agnostic wrapper and integrator to connect diverse machine learning libraries, allowing different frameworks to operate within the same training loop. It uses a remote procedure call orchestrator to manage the exchange of model weights and metadata between a central server and remote workers. The framework covers model aggregation management through interchangeable strategies and supports a custom message bus for transmitting non-standard data packets. It also provides capabilities for performing federated analytics across separate datasets without centralizing the raw information.

An easy-to-use federated learning platform

PySyft is a privacy-preserving machine learning framework and remote computation engine. It functions as a decentralized data analysis orchestrator that allows for the execution of data science workflows on remote servers without requiring the transfer of raw private data from the host device. The platform provides a secure collaboration environment where data owners manage permissions and authorize specific collaborators to run computations. It differentiates its workflow by utilizing mock data for local development and validation before submitting final analysis jobs to private remote servers. The system covers a broad range of secure computation capabilities, including the use of sandboxed job execution to isolate computations from the underlying system and a cloud-storage transport layer for exchanging requests between peers. It also includes mechanisms for asynchronous state synchronization to maintain consistency across offline or cloud-connected nodes.

Federated Learning Made Easy

MONAI is a PyTorch-based deep learning framework and library specifically designed for healthcare imaging. It provides a suite of domain-specific neural network architectures, specialized loss functions, and preprocessing pipelines tailored for analyzing multi-dimensional medical data. The project distinguishes itself through a decentralized federated learning system that allows models to learn from datasets across multiple institutions without exchanging raw patient images. It also features AI-assisted medical image annotation tools and a standardized model bundling system to ensure consistent inference and reproducibility across clinical workstations and cloud environments. The framework covers the full medical AI lifecycle, including data engineering via spatial resampling and normalization, distributed training across multi-GPU nodes, and model evaluation using specialized imaging metrics and result visualization. The library is implemented in Python.

TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads across heterogeneous hardware accelerators and decentralized network nodes. It employs deferred-execution symbolic graphs to perform graph-level optimizations, fusion, and ahead-of-time kernel compilation for specific hardware architectures. To ensure consistent performance across production environments, it features a standardized serialization format for model graphs and specialized tools for model serving, quantization, and compression. Beyond core training capabilities, the platform includes a high-throughput data ingestion engine that supports asynchronous, multi-threaded pipelines to prevent bottlenecks. It also offers extensive support for hardware abstraction, allowing for pluggable device integration and containerized acceleration. The ecosystem is rounded out by utilities for data validation, federated learning, and specialized modeling tasks, providing a complete toolchain for moving models from research into high-availability production environments.