Scalable Training of Differentially Private Recommendation Models through Algorithm-Software Co-Design
LazyDP, an algorithm-software co-design, enables high-throughput training of differentially private recommendation models by addressing the compute and memory bottlenecks of the noise sampling and noisy gradient update operations in DP-SGD.