To appear in the IEEE Journal on Selected Areas in Information Theory (special issue on Information-Theoretic Methods for Trustworthy and Reliable Machine Learning)
We consider the straggler problem in decentralized learning over a logical ring while preserving user data privacy. Especially, we extend the recently proposed framework of differential privacy (DP) amplification by decentralization by Cyffers and Bellet to include overall training latency--comprising both computation and communication latency. Analytical results on both the convergence speed and the DP level are derived for both a skipping scheme (which ignores the stragglers after a timeout) and a baseline scheme that waits for each node to finish before the training continues. A trade-off between overall training latency, accuracy, and privacy, parameterized by the timeout of the skipping scheme, is identified and empirically validated for logistic regression on a real-world dataset and for image classification using the MNIST and CIFAR-10 datasets.
JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning
Department of Information Engineering, The Chinese University of Hong Kong（信息工程系，香港中文大学）；School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society（科学与工程学院，深圳人工智能与机器人研究院）；The Chinese University of Hong Kong（香港中文大学）
DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing
Tsinghua University（清华大学）；Harvard University（哈佛大学）；University of California, Berkeley（加州大学伯克利分校）；Huazhong University of Science and Technology（华中科技大学）；Virginia Tech（弗吉尼亚理工大学）
深度学习技术在广泛的任务中取得了卓越的性能。然而，当在隐私敏感数据集上训练时，模型参数可能暴露训练数据中的隐私信息。之前的差异化私有训练尝试，虽然提供了严格的隐私保证，但导致模型性能比非私有训练低得多。此外，同一训练算法的不同运行产生的模型具有很大的性能差异。为了解决这些问题，我们提出了DPlis--Differentially Private Learning wIth Smoothing。DPlis的核心思想是构造一个平滑的损失函数，该函数有利于位于损失景观的大平坦区域中的抗噪声模型。我们提供了理论依据的实用程序的改进DPlis。大量的实验也表明，DPlis可以有效地提高模型质量和训练稳定性在给定的隐私预算。
CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras
University of Macau（澳门大学）；Tsinghua University（清华大学）；Renmin University of China（中国人民大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Juntendo University（顺天堂大学）；Peking University（北京大学）
