As AI adoption accelerates, safeguarding data privacy in decentralized environments is more critical than ever. This whitepaper explores how Federated Learning (FL) can be fortified using Differential Privacy (DP) and Homomorphic Encryption (HE). While FL enables collaborative model training without raw data sharing, it remains vulnerable to attacks such as gradient leakage and membership inference. By integrating DP and HE, organizations can secure data during model updates and ensure compliance with evolving global privacy regulations like GDPR and HIPAA.
The paper also presents real-world industry risks and showcases cutting-edge solutions powered by VMware and NVIDIA infrastructure, enabling secure, scalable and high-performance FL deployments.
Key Takeaways:
- Federated Learning isn't inherently secure , techniques like DP and HE are essential to prevent data leakage.
- Differential privacy adds mathematical privacy guarantees, mitigating risks like membership inference and reconstruction attacks.
- Homomorphic encryption enables computations on encrypted data, securing model updates without compromising performance.
Download the full whitepaper to explore how your enterprise can leverage privacy-preserving Federated Learning powered by HCLTech’s AI infrastructure.
