Distributed Functional Mechanism in Shallow Networks: Differential Privacy Without Gradient Noise

Researchers proposed DFM (Distributed Functional Mechanism), a method for training AI models while protecting user privacy in systems where multiple people contribute data. Unlike older privacy-focused training methods like DP-SGD (differentially private stochastic gradient descent, which adds noise to the mathematical directions the model learns), DFM protects privacy by adding noise to polynomial approximations (simplified mathematical descriptions) of the training process, making it faster and more stable while maintaining privacy guarantees across all users.