Federated Learning Frameworks

Fl framework assembly

EasyFL is a low-code federated learning platform for experimentation and prototyping, supporting heterogeneity simulation, training flow abstraction, and distributed training optimization. Limitations include focus on experimentation and ongoing development.

Fl framework assembly

FATE is an industrial-grade federated learning framework supporting secure computation protocols, modular design, and large-scale deployments. Limitations include complexity and resource requirements.

Fl framework assembly

FLSim is a domain-agnostic, scalable federated learning simulation framework supporting differential privacy, secure aggregation, and compression techniques. Limitations include focus on simulation and ongoing development.

Fl framework assembly

FedLab is a flexible, modular federated learning framework for research and simulation, supporting diverse data distributions and algorithms. Limitations include effectiveness in highly heterogeneous environments and operational constraints.

Fl framework assembly

FedML is a versatile federated learning library supporting cross-device, cross-silo, and edge-cloud FL, with comprehensive benchmarks and open collaboration. Limitations include complexity for beginners and resource intensity.

Fl framework assembly

FedScale is a federated learning benchmark and runtime offering realistic FL tasks, scalable runtime, and diverse datasets for heterogeneity-aware research. Limitations include focus on benchmarking and ongoing development.

Fl framework assembly

FederatedScope is a modular, extensible federated learning platform with an event-driven architecture, supporting a wide range of FL applications and privacy protection. Limitations include a learning curve for new users and ongoing development.

Fl framework assembly

Flower is a highly customizable, scalable, and framework-agnostic federated learning framework for research and production. Limitations include integration complexity and scalability challenges in very large deployments.

Fl framework assembly

Galaxy Federated Learning is a decentralized FL framework based on blockchain technology, designed to maintain data ownership and model interest while improving bandwidth utilization and security. Limitations include ongoing development and novel approach challenges.

Fl framework assembly

IBM Federated Learning is a highly configurable and extensible FL framework supporting a wide range of ML techniques and fusion algorithms. Limitations include significant setup and configuration requirements.

Fl framework assembly

MetisFL is a modular, extensible, and configurable federated learning framework supporting TensorFlow and PyTorch, designed for general-purpose FL across domains. Limitations include challenges with niche requirements and ongoing development.

Fl framework assembly

NVFlare is a privacy-preserving federated learning framework supporting popular ML/DL frameworks, with tools for secure provisioning, orchestration, and monitoring. Limitations include implementation challenges in domain-specific applications.

Fl framework assembly

OpenFL is a scalable, secure, and framework-agnostic federated learning library supporting large federations and privacy-preserving analytics. Limitations include setup complexity and requirement for familiarity with security features.

Fl framework assembly

PaddleFL is a federated learning framework supporting horizontal and vertical FL strategies, leveraging PaddlePaddle for distributed training and Kubernetes for deployment. Limitations include ongoing development for vertical FL and deployment schemes.

Fl framework assembly

Plato is a scalable, extensible federated learning research framework supporting a wide range of FL algorithms and real-world system implementations. Limitations include focus on research and ongoing development.

Fl framework assembly

PySyft enables secure and private data science on data you cannot see, supporting PETs, encrypted operations, and federated learning. Limitations include complexity for new users and dependency on active community support.

Fl framework assembly

TensorFlow Federated (TFF) is an open-source framework for machine learning and analytics on decentralized data, supporting custom federated algorithms and analytics. Limitations include a requirement for TensorFlow familiarity and limited flexibility with non-TensorFlow technologies.