LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
<|reference_start|>LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models: Differential privacy (DP) is widely being employed in the industry as a practical standard for privacy protection. While private training of computer vision or natural language processing applications has been studied extensively, the computational challenges of training of recommender systems (RecSys) with DP have not been explored. In this work, we first present our detailed characterization of private RecSys training using DP-SGD, root-causing its several performance bottlenecks. Specifically, we identify DP-SGD's noise sampling and noisy gradient update stage to suffer from a severe compute and memory bandwidth limitation, respectively, causing significant performance overhead in training private RecSys. Based on these findings, we propose LazyDP, an algorithm-software co-design that addresses the compute and memory challenges of training RecSys with DP-SGD. Compared to a state-of-the-art DP-SGD training system, we demonstrate that LazyDP provides an average 119x training throughput improvement while also ensuring mathematically equivalent, differentially private RecSys models to be trained.<|reference_end|>
@article{lim2024lazydp:,
    title={LazyDP: Co-Designing Algorithm-Software for Scalable Training of
  Differentially Private Recommendation Models},
    author={Juntaek Lim, Youngeun Kwon, Ranggi Hwang, Kiwan Maeng, G. Edward Suh,
  Minsoo Rhu},
    journal={Published at 29th ACM International Conference on Architectural
  Support for Programming Languages and Operating Systems (ASPLOS-29), 2024},
    year={2024},
    doi={10.1145/3620665.3640384},
    archivePrefix={arXiv},
    eprint={2404.08847},
    primaryClass={cs.IR cs.CR cs.LG}
}