New Federated Learning Framework Combines Differential Privacy and Secure Aggregation for Enhanced Data Protection
Researchers have developed DDP-SA, a federated learning framework that combines local differential privacy with secure aggregation to protect individual data contributions during distributed machine learning. The approach uses a two-stage process where clients add noise to their data before it is split into secret shares across multiple servers, ensuring no single server can access raw information. This work addresses a key challenge in privacy-preserving machine learning by balancing strong privacy guarantees with practical computational efficiency.
The DDP-SA framework integrates two complementary privacy techniques: client-side local differential privacy (LDP), which adds calibrated noise to individual gradient updates, and full-threshold additive secret sharing (ASS), which distributes noisy gradients across multiple intermediate servers. This dual-layer approach ensures that neither individual servers nor communication channels can expose client-specific data, while the parameter server only ever reconstructs aggregated noisy gradients. According to the research, the framework achieves higher model accuracy than methods using differential privacy alone, while providing stronger privacy protections than approaches relying solely on secure multi-party computation. The design scales linearly with the number of participants and maintains practical computational and communication overhead, making it suitable for real-world federated learning applications where privacy is critical.
What's missing
The paper does not discuss specific real-world deployment scenarios, comparison with other hybrid privacy approaches beyond MPC-only baselines, or empirical evaluation against potential adversarial attacks targeting the two-stage mechanism.
What different sources said
- arXiv cs.LGCenter
FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
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