Secure Data Sharing with Federated Learning

[Ayaanshaikh12243]

Pull Request: Secure Data Sharing with Federated Learning

Issue Reference

Closes #994

Summary

This PR implements a federated learning infrastructure for secure, privacy-preserving data sharing and collaborative analytics across departments or partners. Raw financial data remains local, and only model updates are exchanged, ensuring compliance and minimizing privacy risks.

Features

• Federated server orchestration
• Federated client for local model training
• Secure communication (encryption, authentication)
• Differential privacy and secure aggregation
• Financial analytics model logic
• Compliance logging and audit trail
• Modular configuration and setup

Implementation Details

• New federated/ directory with server, client, crypto, privacy, model, compliance, and config modules

• Audit log for compliance and traceability

• Example usage and extensibility for other analytics models

Testing

• Manual tests for server/client communication
• Model update and aggregation verified
• Compliance logs generated and reviewed

Documentation

• Inline comments in all modules
• Example configuration in federated/config.js

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[github-actions]

🎉 Thanks for the PR, @Ayaanshaikh12243!

We really appreciate you taking the time to contribute to ExpenseFlow! 💙

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✅ PR Checklist

Before we review, please ensure:

• Your code follows the project's coding standards
• All file changes are accurate and intentional
• You've tested your changes locally
• Any review comments have been addressed

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🙌 Thank You for Contributing!

We truly appreciate your interest in contributing to this project.

• Please make sure your code follows the project structure
• Add clear commit messages and comments where necessary
• Ensure your changes do not break existing functionality

We'll review your PR as soon as possible. Keep up the great work! ✨

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Project	Deployment	Actions	Updated (UTC)
expenseflow	Error		Mar 6, 2026 8:55am