Privacy-Preserving Federated Learning is the combination of federated learning with privacy-enhancing technologies — ensuring that not only is raw data kept local, but also that the gradient updates shared with the server do not leak private information about individual training examples.

Privacy Enhancements for FL

• Differential Privacy (DP): Add calibrated noise to gradient updates before sharing — provides formal privacy guarantees.
• Secure Aggregation: Cryptographically aggregate gradients so the server only sees the sum, not individual updates.
• Homomorphic Encryption: Encrypt gradient updates — the server aggregates encrypted gradients without decryption.
• Gradient Compression: Compress gradients to reduce information leakage (and communication cost).

Why It Matters

• FL Alone Leaks: Standard FL gradient updates can be inverted to reconstruct training data (gradient inversion attacks).
• Regulatory Compliance: GDPR, HIPAA, and industry regulations require provable privacy protections.
• Semiconductor: Multi-fab collaborative training requires strong privacy — each fab's process data is highly confidential.

Privacy-Preserving FL is federated learning with mathematical privacy guarantees — ensuring gradient updates don't leak private training data.