privacy-preserving federated learning

**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.