Daniel Ramage
Daniel has been at Google since 2011 focusing on federated learning and privacy technologies. Additional publications available on Google Scholar.
Authored Publications
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Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications
Yanxiang Zhang
Zheng Xu
Yuanbo Zhang
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) (2025)
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Error correction is an important capability when applying large language models (LLMs) to facilitate user typing on mobile devices. In this paper, we use LLMs to synthesize a high-quality dataset of error correction pairs to evaluate and improve LLMs for mobile applications. We first prompt LLMs with error correction domain knowledge to build a scalable and reliable addition to the existing data synthesis pipeline. We then adapt the synthetic data distribution to match the mobile application domain by reweighting the samples. The reweighting model is learnt by predicting (a handful of) live A/B test metrics when deploying LLMs in production, given the LLM performance on offline evaluation data and scores from a small privacy-preserving on-device language model. Finally, we present best practices for mixing our synthetic data with other data sources to improve model performance on error correction in both offline evaluation and production live A/B testing.
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Prompt Public Large Language Models to Synthesize Data for Private On-device Applications
Zheng Xu
Yanxiang Zhang
Yuanbo Zhang
Conference on Language Modeling (COLM) (2024)
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Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP). This paper investigates how large language models (LLMs) trained on public data can improve the quality of pre-training data for the on-device language models trained with DP and FL. We carefully design LLM prompts to filter and transform existing public data, and generate new data to resemble the real user data distribution. The model pre-trained on our synthetic dataset achieves relative improvement of 19.0\% and 22.8\% in next word prediction accuracy compared to the baseline model pre-trained on a standard public dataset, when evaluated over the real user data in Gboard (Google Keyboard, a production mobile keyboard application). Furthermore, our method achieves evaluation accuracy better than or comparable to the baseline during the DP FL fine-tuning over the user data from millions of mobile devices, and our final model outperforms the baseline in production A/B testing. Our experiments demonstrate the strengths of LLMs in synthesizing data close to the private distribution even without accessing the private data, and also suggest future directions of improvements to further reduce the distribution gap.
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Confidential Federated Computations
Hubert Eichner
Dzmitry Huba
Brett McLarnon
Timon Van Overveldt
Nova Fallen
Albert Cheu
Katharine Daly
Adria Gascon
Marco Gruteser
ArXiv (2024)
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Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data. However, basic FLA systems have privacy limitations: they do not necessarily require anonymization mechanisms like differential privacy (DP), and provide limited protections against a potentially malicious service provider. Adding DP to a basic FLA system currently requires either adding excessive noise to each device's updates, or assuming an honest service provider that correctly implements the mechanism and only uses the privatized outputs. Secure multiparty computation (SMPC) -based oblivious aggregations can limit the service provider's access to individual user updates and improve DP tradeoffs, but the tradeoffs are still suboptimal, and they suffer from scalability challenges and susceptibility to Sybil attacks. This paper introduces a novel system architecture that leverages trusted execution environments (TEEs) and open-sourcing to both ensure confidentiality of server-side computations and provide externally verifiable privacy properties, bolstering the robustness and trustworthiness of private federated computations.
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Building privacy-preserving systems for machine learning and data science on decentralized data
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Generative Models for Effective ML on Private, Decentralized Datasets
8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net
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To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact. Manual inspection of raw data—of representative samples, of outliers, of misclassifications—is an essential tool in a) identifying and fixing problems in the data, b) generating new modeling hypotheses, and c) assigning or refining human-provided labels. However, manual data inspection is risky for privacy-sensitive datasets, such as those representing the behavior of real-world individuals. Furthermore, manual data inspection is impossible in the increasingly important setting of federated learning, where raw examples are stored at the edge and the modeler may only access aggregated outputs such as metrics or model parameters. This paper demonstrates that generative models—trained using federated methods and with formal differential privacy guarantees—can be used effectively to debug data issues even when the data cannot be directly inspected. We explore these methods in applications to text with differentially private federated RNNs and to images using a novel algorithm for differentially private federated GANs.
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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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Federated Learning for Mobile Keyboard Prediction
Chloé M Kiddon
Françoise Simone Beaufays
Hubert Eichner
(2019)
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We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones. Server-based training using stochastic gradient descent is compared with training on client devices using the Federated Averaging algorithm. The federated algorithm, which enables training on a higher-quality dataset for this use case, is shown to achieve better prediction recall. This work demonstrates the feasibility and benefit of training language models on client devices without exporting sensitive user data to servers. The federated learning environment gives users greater control over their data and simplifies the task of incorporating privacy by default with distributed training and aggregation across a population of client devices.
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Advances and Open Problems in Federated Learning
Brendan Avent
Aurélien Bellet
Mehdi Bennis
Arjun Nitin Bhagoji
Graham Cormode
Rachel Cummings
Rafael G.L. D'Oliveira
Salim El Rouayheb
David Evans
Josh Gardner
Adrià Gascón
Phillip B. Gibbons
Marco Gruteser
Zaid Harchaoui
Chaoyang He
Lie He
Zhouyuan Huo
Justin Hsu
Martin Jaggi
Tara Javidi
Gauri Joshi
Mikhail Khodak
Jakub Konečný
Aleksandra Korolova
Farinaz Koushanfar
Sanmi Koyejo
Tancrède Lepoint
Yang Liu
Prateek Mittal
Richard Nock
Ayfer Özgür
Rasmus Pagh
Ramesh Raskar
Dawn Song
Weikang Song
Sebastian U. Stich
Ziteng Sun
Florian Tramèr
Praneeth Vepakomma
Jianyu Wang
Li Xiong
Zheng Xu
Qiang Yang
Felix X. Yu
Han Yu
Arxiv (2019)
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Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and mitigates many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents a comprehensive list of open problems and challenges.
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Federated Evaluation of On-device Personalization
Chloé M Kiddon
Françoise Simone Beaufays
Hubert Eichner
Kangkang Wang
(2019)
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Federated learning is a distributed, on-device computation framework that enables training global models without exporting sensitive user data to servers. In this work, we describe methods to extend the federation framework to evaluate strategies for personalization of global models. We present tools to analyze the effects of personalization and evaluate conditions under which personalization yields desirable models. We report on our experiments personalizing a language model for a virtual keyboard for smartphones with a population of tens of millions of users. We show that a significant fraction of users benefit from personalization.
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Towards Federated Learning at Scale: System Design
Hubert Eichner
Wolfgang Grieskamp
Dzmitry Huba
Vladimir Ivanov
Chloé M Kiddon
Jakub Konečný
Stefano Mazzocchi
Timon Van Overveldt
David Petrou
Jason Roselander
SysML 2019
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Federated Learning is a distributed machine learning approach which enables training on a large corpus of data which never needs to leave user devices. We have spent some effort over the last two years building a scalable production system for FL. In this paper, we report about the resulting high-level design, sketching the challenges and the solutions, as well as touching the open problems and future directions.
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