EasyFL

Overview

EasyFL is an open-source federated learning platform that simplifies the setup and execution of distributed machine learning experiments. It provides a unified interface for simulating federated training across multiple clients, supporting common aggregation algorithms and data partitioning strategies.

Best for

Best for
Researchers and students exploring federated learning concepts in a simulated setting

Use cases

• Simulating federated learning workflows with custom data splits
• Benchmarking aggregation algorithms like FedAvg or FedProx
• Prototyping privacy-preserving distributed ML models

Notes

EasyFL is an open-source federated learning platform that simplifies the setup and execution of distributed machine learning experiments. It provides a unified interface for simulating federated training across multiple clients, supporting common aggregation algorithms and data partitioning strategies.

25 stars on GitHub. Last updated 2023-08-23. Licensed Apache-2.0.

Use cases

• Simulating federated learning workflows with custom data splits
• Benchmarking aggregation algorithms like FedAvg or FedProx
• Prototyping privacy-preserving distributed ML models

Pros

• Low barrier to entry with a straightforward API for federated learning
• Supports multiple aggregation strategies out of the box
• Active community with open-source codebase for customization

Cons

• Limited to simulation environments, not production-grade deployment
• Small community with only 25 GitHub stars, so limited support and documentation
• Lacks advanced features like secure aggregation or differential privacy

Indexed from awesome-llmops and enriched against its public facts.