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Secure Data Sharing with Federated Learning#1012

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Ayaanshaikh12243 wants to merge 1 commit intoRenu-code123:mainfrom
Ayaanshaikh12243:ISSUE-994
Open

Secure Data Sharing with Federated Learning#1012
Ayaanshaikh12243 wants to merge 1 commit intoRenu-code123:mainfrom
Ayaanshaikh12243:ISSUE-994

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@Ayaanshaikh12243
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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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vercel bot commented Mar 5, 2026

@Ayaanshaikh12243 is attempting to deploy a commit to the Renu's projects Team on Vercel.

A member of the Team first needs to authorize it.

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github-actions bot commented Mar 5, 2026

🎉 Thanks for the PR, @Ayaanshaikh12243!

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


⭐ Love this project?

Please give us a star! It helps the project grow and reach more developers! 🌟

🔗 https://github.com/Renu-code123/ExpenseFlow


✅ 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

🙌 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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Secure Data Sharing with Federated Learning

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