federated-learning

What problem does it solve?

This Skill addresses the challenges of training machine learning models on decentralized data while preserving privacy and managing model drift in quantitative research and production environments.

Core Features & Use Cases

• Distributed Model Training: Enables model updates across multiple data silos without centralizing sensitive data.
• Privacy Management: Incorporates privacy budgets to control information leakage during training rounds.
• Drift Detection & Management: Monitors for and addresses changes in data distributions or model performance over time.
• Use Case: Apply this Skill when developing a trading strategy using data from various brokers, ensuring each broker's data remains private while contributing to a global, robust model.

Quick Start

Run federated learning diagnostics on the input data file 'input.csv'.