Peter Kairouz
Peter Kairouz is a researcher interested in machine learning, security, and privacy. At Google, he is a Research Scientist working on decentralized and privacy-preserving machine learning algorithms. Prior to Google, his doctoral and postdoctoral research have largely focused on building decentralized technologies for anonymous broadcasting over complex networks, understanding the fundamental trade-off between data privacy and utility, and leveraging state-of-the-art deep generative models for data-driven privacy. You can learn more about his background and research by visiting his Stanford webpage. Some of his recent Google publications are listed below.
Authored Publications
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Improved Communication-Privacy Trade-offs in L2 Mean Estimation under Streaming Differential Privacy
Wei-Ning Chen
Albert No
Sewoong Oh
International Conference on Machine Learning (ICML) (2024)
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We study $L_2$ mean estimation under central differential privacy and communication constraints, and address two key challenges: firstly, existing mean estimation schemes that simultaneously handle both constraints are usually optimized for $L_\infty$ geometry and rely on random rotation or Kashin's representation to adapt to $L_2$ geometry, resulting in suboptimal leading constants in mean square errors (MSEs); secondly, schemes achieving order-optimal communication-privacy trade-offs do not extend seamlessly to streaming differential privacy (DP) settings (e.g., tree aggregation or matrix factorization), rendering them incompatible with DP-FTRL type optimizers.
In this work, we tackle these issues by introducing a novel privacy accounting method for the sparsified Gaussian mechanism that incorporates the randomness inherent in sparsification into the DP noise. Unlike previous approaches, our accounting algorithm directly operates in $L_2$ geometry, yielding MSEs that fast converge to those of the uncompressed Gaussian mechanism. Additionally, we extend the sparsification scheme to the matrix factorization framework under streaming DP and provide a precise accountant tailored for DP-FTRL type optimizers. Empirically, our method demonstrates at least a 100x improvement of compression for DP-SGD across various FL tasks.
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Service providers of large language model (LLM) applications collect user instructions in the wild and use them in further aligning LLMs with users’ intentions. These instructions, which potentially contain sensitive information, are annotated by human workers in the process. This poses a new privacy risk not addressed by the typical private optimization. To this end, we propose using synthetic instructions to replace real instructions in data annotation and model fine-tuning. Formal differential privacy is guaranteed by generating those synthetic instructions using privately fine-tuned generators. Crucial in achieving the desired utility is our novel filtering algorithm that matches the distribution of the synthetic instructions to that of the real ones. In both supervised fine-tuning and reinforcement learning from human feedback, our extensive experiments demonstrate the high utility of the final set of synthetic instructions by showing comparable results to real instructions. In supervised fine-tuning, models trained with private synthetic instructions outperform leading open-source models such as Vicuna
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Federated Learning of Gboard Language Models with Differential Privacy
Yanxiang Zhang
Galen Andrew
Jesse Rosenstock
Yuanbo Zhang
ACL industry track (2023) (to appear)
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We train language models (LMs) with federated learning (FL) and differential privacy (DP) in the Google Keyboard (Gboard). We apply the DP-Follow-the-Regularized-Leader (DP-FTRL)~\citep{kairouz21b} algorithm to achieve meaningfully formal DP guarantees without requiring uniform sampling of client devices.
To provide favorable privacy-utility trade-offs, we introduce a new client participation criterion and discuss the implication of its configuration in large scale systems. We show how quantile-based clip estimation~\citep{andrew2019differentially} can be combined with DP-FTRL to adaptively choose the clip norm during training or reduce the hyperparameter tuning in preparation for training.
With the help of pretraining on public data, we train and deploy more than twenty Gboard LMs that achieve high utility and $\rho-$zCDP privacy guarantees with $\rho \in (0.2, 2)$, with two models additionally trained with secure aggregation~\citep{bonawitz2017practical}.
We are happy to announce that all the next word prediction neural network LMs in Gboard now have DP guarantees, and all future launches of Gboard neural network LMs will require DP guarantees.
We summarize our experience and provide concrete suggestions on DP training for practitioners.
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Practical and Private (Deep) Learning without Sampling or Shuffling
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Om Thakkar
Abhradeep Thakurta
38th International Conference on Machine Learning (ICML 2021) (2021) (to appear)
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Building privacy-preserving systems for machine learning and data science on decentralized data
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A Field Guide to Federated Optimization
Jianyu Wang
Gauri Joshi
Maruan Al-Shedivat
Galen Andrew
A. Salman Avestimehr
Katharine Daly
Deepesh Data
Suhas Diggavi
Hubert Eichner
Advait Gadhikar
Antonious M. Girgis
Filip Hanzely
Chaoyang He
Samuel Horvath
Martin Jaggi
Tara Javidi
Satyen Chandrakant Kale
Sai Praneeth Karimireddy
Jakub Konečný
Sanmi Koyejo
Tian Li
Peter Richtarik
Karan Singhal
Virginia Smith
Mahdi Soltanolkotabi
Weikang Song
Sebastian Stich
Ameet Talwalkar
Hongyi Wang
Blake Woodworth
Honglin Yuan
Mi Zhang
Tong Zhang
Chunxiang (Jake) Zheng
Chen Zhu
arxiv (2021)
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Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving federated optimization problems, which emphasize communication efficiency, data heterogeneity, compatibility with privacy and system requirements, and other constraints that are not primary considerations in other problem settings. This paper provides recommendations and guidelines on formulating, designing, evaluating and analyzing federated optimization algorithms through concrete examples and practical implementation, with a focus on conducting effective simulations to infer real-world performance. The goal of this work is not to survey the current literature, but to inspire researchers and practitioners to design federated learning algorithms that can be used in various practical applications.
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Privacy-first Health Research with Federated Learning
Adam Sadilek
Dung Nguyen
Methun Kamruzzaman
Benjamin Rader
Stefan Mellem
Elaine O. Nsoesie
Jamie MacFarlane
Anil Vullikanti
Madhav Marathe
Paul C. Eastham
John S. Brownstein
John Hernandez
npj Digital Medicine (2021)
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Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion. These techniques facilitate the calculation of research study endpoints such that private data never leaves a given device or healthcare system. We show—on a diverse set of single and multi-site health studies—that federated models can achieve similar accuracy, precision, and generalizability, and lead to the same interpretation as standard centralized statistical models while achieving considerably stronger privacy protections and without significantly raising computational costs. This work is the first to apply modern and general federated learning methods that explicitly incorporate differential privacy to clinical and epidemiological research—across a spectrum of units of federation, model architectures, complexity of learning tasks and diseases. As a result, it enables health research participants to remain in control of their data and still contribute to advancing science—aspects that used to be at odds with each other.
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Context-Aware Local Differential Privacy
Jayadev Acharya
Ziteng Sun
International Conference on Machine Learning (ICML) (2020)
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Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility. The classical definition of LDP assumes that all elements in the data domain are equally sensitive. However, in many applications, some symbols are more sensitive than others. This work proposes a context-aware framework of local differential privacy that allows a privacy designer to incorporate the application's context into the privacy definition. For binary data domains, we provide a universally optimal privatization scheme and highlight its connections to Warner's randomized response (RR) and Mangat's improved response. Motivated by geolocation and web search applications, for k-ary data domains, we consider two special cases of context-aware LDP: block-structured LDP and high-low LDP. We study discrete distribution estimation and provide communication-efficient, sample-optimal schemes and information-theoretic lower bounds for both models. We show that using contextual information can require fewer samples than classical LDP to achieve the same accuracy.
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Privacy Amplification via Random Check-Ins
Borja Balle
Om Thakkar
Abhradeep Thakurta
Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020
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Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fundamental building block in many applications for learning over sensitive data. Two standard approaches, privacy amplification by subsampling, and privacy amplification by shuffling, permit adding lower noise in DP-SGD than via na\"{\i}ve schemes. A key assumption in both these approaches is that the elements in the data set can be uniformly sampled, or be uniformly permuted --- constraints that may become prohibitive when the data is processed in a decentralized or distributed fashion. In this paper, we focus on conducting iterative methods like DP-SGD in the setting of federated learning (FL) wherein the data is distributed among many devices (clients). Our main contribution is the random check-in distributed protocol, which crucially relies only on randomized participation decisions made locally and independently by each client. It has privacy/accuracy trade-offs similar to privacy amplification by subsampling/shuffling. However, our method does not require server-initiated communication, or even knowledge of the population size. To our knowledge, this is the first privacy amplification tailored for a distributed learning framework, and it may have broader applicability beyond FL. Along the way, we extend privacy amplification by shuffling to incorporate $(\epsilon,\delta)$-DP local randomizers, and exponentially improve its guarantees. In practical regimes, this improvement allows for similar privacy and utility using data from an order of magnitude fewer users.
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Federated Heavy Hitters with Differential Privacy
Haicheng Sun
Vivian (Wei) Li
International Conference on Artificial Intelligence and Statistics (AISTATS) 2020
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The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but can incur substantial privacy risks if not done with care. To address these risks, we propose a distributed and privacy-preserving algorithm for discovering the heavy hitters in a population of user-generated data streams. We leverage the sampling property of our distributed algorithm to prove that it is inherently differentially private, without requiring additional noise. We also examine the trade-off between privacy and utility, and show that our algorithm provides excellent utility while also achieving strong privacy guarantees. A significant advantage of this approach is that it eliminates the need to centralize raw data while also avoiding the significant loss in utility incurred by local differential privacy. We validate our findings both theoretically, using worst-case analyses, and practically, using a Twitter dataset with 1.6M tweets and over 650k users. Finally, we carefully compare our approach to Apple's local differential privacy method for discovering heavy hitters.
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