Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Abstract
Lay Summary
Protecting user privacy in collaborative AI training (federated learning) requires adding carefully designed noise. However, this noise can unevenly disrupt different parts of each device's learned knowledge over time – like obscuring facial features in one device's animal recognition model while blurring limb details in another's.We introduce FedCEO, a new approach where devices "Collaborate with Each Other" under server coordination. FedCEO intelligently combines the complementary knowledge from all devices. When one device's understanding of a concept is disrupted by privacy protection, others help fill those gaps.This CEO-like coordination gradually enhances semantic smoothness across devices as training progresses. The server blends the partial understandings into a coherent whole, allowing the global model to recover disrupted patterns while maintaining privacy. The result is significantly improved AI performance across diverse privacy settings and extended training periods.