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Article ; Online: Author Correction: Systemic sclerosis gastrointestinal dysmotility: risk factors, pathophysiology, diagnosis and management.

McMahan, Zsuzsanna H / Kulkarni, Subhash / Chen, Joan / Chen, Jiande Z / Xavier, Ramnik J / Pasricha, P Jay / Khanna, Dinesh

Nature reviews. Rheumatology

2023 Volume 19, Issue 3, Page(s) 191

Language	English
Publishing date	2023-02-15
Publishing country	United States
Document type	Published Erratum
ZDB-ID	2491532-4
ISSN	1759-4804 ; 1759-4790
ISSN (online)	1759-4804
ISSN	1759-4790
DOI	10.1038/s41584-023-00929-1
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Article: Managing gastrointestinal complications in patients with systemic sclerosis.

McMahan, Z H / Khanna, D

Current treatment options in gastroenterology

2020 Volume 18, Page(s) 531–544

Abstract: Objective: We sought to critically evaluate the literature published over the past 3 years on the management of gastrointestinal complications in systemic sclerosis (SSc). We emphasize interesting and important new findings to bring the reader up-to- ... ...

Abstract	Objective: We sought to critically evaluate the literature published over the past 3 years on the management of gastrointestinal complications in systemic sclerosis (SSc). We emphasize interesting and important new findings to bring the reader up-to-date. We also discuss controversial discoveries and hypotheses currently of interest. Methods: We conducted a literature search on PubMed over the last 3 years using the key words "systemic sclerosis," "gastrointestinal," "scleroderma," and "treatment." We also screened clinicaltrials.gov for ongoing trials relevant to the gastrointestinal complications of SSc. Reference lists from recent reviews on the management of gastrointestinal complications of SSc to identify articles that might have been missed in the initial search. Results: 103 publications and ongoing clinical trials were identified. We eliminated all case reports and review articles. Ultimately we had 58 articles remaining and we prioritized what we found to be the strongest and/or novel findings to discuss in this review. Conclusions: Advances in the management of gastrointestinal disease in SSc continue to evolve. The application of novel therapies and the repurposing of existing therapies for the management of gastrointestinal involvement are shaping the therapeutic arsenal so that we can more effectively manage these complex patients.
Language	English
Publishing date	2020-11-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	2057334-0
ISSN	1534-309X ; 1092-8472
ISSN (online)	1534-309X
ISSN	1092-8472
DOI	10.1007/s11938-020-00314-x
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Article ; Online: Systemic sclerosis gastrointestinal dysmotility: risk factors, pathophysiology, diagnosis and management.

McMahan, Zsuzsanna H / Kulkarni, Subhash / Chen, Joan / Chen, Jiande Z / Xavier, Ramnik J / Pasricha, P Jay / Khanna, Dinesh

Nature reviews. Rheumatology

2023 Volume 19, Issue 3, Page(s) 166–181

Abstract: Nearly all patients with systemic sclerosis (SSc) are negatively affected by dysfunction in the gastrointestinal tract, and the severity of gastrointestinal disease in SSc correlates with high mortality. The clinical complications of this dysfunction are ...

Abstract	Nearly all patients with systemic sclerosis (SSc) are negatively affected by dysfunction in the gastrointestinal tract, and the severity of gastrointestinal disease in SSc correlates with high mortality. The clinical complications of this dysfunction are heterogeneous and include gastro-oesophageal reflux disease, gastroparesis, small intestinal bacterial overgrowth, intestinal pseudo-obstruction, malabsorption and the requirement for total parenteral nutrition. The abnormal gastrointestinal physiology that promotes the clinical manifestations of SSc gastrointestinal disease throughout the gastrointestinal tract are diverse and present a range of therapeutic targets. Furthermore, the armamentarium of medications and non-pharmacological interventions that can benefit affected patients has substantially expanded in the past 10 years, and research is increasingly focused in this area. Here, we review the details of the gastrointestinal complications in SSc, tie physiological abnormalities to clinical manifestations, detail the roles of standard and novel therapies and lay a foundation for future investigative work.
MeSH term(s)	Humans ; Gastrointestinal Diseases/diagnosis ; Gastrointestinal Diseases/etiology ; Gastrointestinal Diseases/therapy ; Scleroderma, Systemic/complications ; Risk Factors
Language	English
Publishing date	2023-02-06
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural
ZDB-ID	2491532-4
ISSN	1759-4804 ; 1759-4790
ISSN (online)	1759-4804
ISSN	1759-4790
DOI	10.1038/s41584-022-00900-6
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Book ; Online: How to DP-fy ML

Ponomareva, Natalia / Hazimeh, Hussein / Kurakin, Alex / Xu, Zheng / Denison, Carson / McMahan, H. Brendan / Vassilvitskii, Sergei / Chien, Steve / Thakurta, Abhradeep

A Practical Guide to Machine Learning with Differential Privacy

2023

Abstract: ML models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the privacy of ML training data. Differential Privacy (DP) has become a gold ... ...

Abstract	ML models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the privacy of ML training data. Differential Privacy (DP) has become a gold standard for making formal statements about data anonymization. However, while some adoption of DP has happened in industry, attempts to apply DP to real world complex ML models are still few and far between. The adoption of DP is hindered by limited practical guidance of what DP protection entails, what privacy guarantees to aim for, and the difficulty of achieving good privacy-utility-computation trade-offs for ML models. Tricks for tuning and maximizing performance are scattered among papers or stored in the heads of practitioners. Furthermore, the literature seems to present conflicting evidence on how and whether to apply architectural adjustments and which components are "safe" to use with DP. This work is a self-contained guide that gives an in-depth overview of the field of DP ML and presents information about achieving the best possible DP ML model with rigorous privacy guarantees. Our target audience is both researchers and practitioners. Researchers interested in DP for ML will benefit from a clear overview of current advances and areas for improvement. We include theory-focused sections that highlight important topics such as privacy accounting and its assumptions, and convergence. For a practitioner, we provide a background in DP theory and a clear step-by-step guide for choosing an appropriate privacy definition and approach, implementing DP training, potentially updating the model architecture, and tuning hyperparameters. For both researchers and practitioners, consistently and fully reporting privacy guarantees is critical, and so we propose a set of specific best practices for stating guarantees.
Keywords	Computer Science - Machine Learning ; Computer Science - Cryptography and Security ; Statistics - Machine Learning
Subject code	330
Publishing date	2023-03-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: (Amplified) Banded Matrix Factorization

Choquette-Choo, Christopher A. / Ganesh, Arun / McKenna, Ryan / McMahan, H. Brendan / Rush, Keith / Thakurta, Abhradeep / Xu, Zheng

A unified approach to private training

2023

Abstract: Matrix factorization (MF) mechanisms for differential privacy (DP) have substantially improved the state-of-the-art in privacy-utility-computation tradeoffs for ML applications in a variety of scenarios, but in both the centralized and federated settings ...

Abstract	Matrix factorization (MF) mechanisms for differential privacy (DP) have substantially improved the state-of-the-art in privacy-utility-computation tradeoffs for ML applications in a variety of scenarios, but in both the centralized and federated settings there remain instances where either MF cannot be easily applied, or other algorithms provide better tradeoffs (typically, as $\epsilon$ becomes small). In this work, we show how MF can subsume prior state-of-the-art algorithms in both federated and centralized training settings, across all privacy budgets. The key technique throughout is the construction of MF mechanisms with banded matrices (lower-triangular matrices with at most $\hat{b}$ nonzero bands including the main diagonal). For cross-device federated learning (FL), this enables multiple-participations with a relaxed device participation schema compatible with practical FL infrastructure (as demonstrated by a production deployment). In the centralized setting, we prove that banded matrices enjoy the same privacy amplification results as the ubiquitous DP-SGD algorithm, but can provide strictly better performance in most scenarios -- this lets us always at least match DP-SGD, and often outperform it. Comment: 34 pages, 13 figures
Keywords	Computer Science - Machine Learning ; Computer Science - Cryptography and Security
Subject code	005
Publishing date	2023-06-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Can Public Large Language Models Help Private Cross-device Federated Learning?

Wang, Boxin / Zhang, Yibo Jacky / Cao, Yuan / Li, Bo / McMahan, H. Brendan / Oh, Sewoong / Xu, Zheng / Zaheer, Manzil

2023

Abstract: We study (differentially) private federated learning (FL) of language models. The language models in cross-device FL are relatively small, which can be trained with meaningful formal user-level differential privacy (DP) guarantees when massive ... ...

Abstract	We study (differentially) private federated learning (FL) of language models. The language models in cross-device FL are relatively small, which can be trained with meaningful formal user-level differential privacy (DP) guarantees when massive parallelism in training is enabled by the participation of a moderate size of users. Recently, public data has been used to improve privacy-utility trade-offs for both large and small language models. In this work, we provide a systematic study of using large-scale public data and LLMs to help differentially private training of on-device FL models, and further improve the privacy-utility tradeoff by techniques of distillation. Moreover, we propose a novel distribution matching algorithm with theoretical grounding to sample public data close to private data distribution, which significantly improves the sample efficiency of (pre-)training on public data. The proposed method is efficient and effective for training private model by taking advantage of public data, especially for customized on-device architectures that do not have ready-to-use pre-trained models.
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-05-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Federated Learning of Gboard Language Models with Differential Privacy

Xu, Zheng / Zhang, Yanxiang / Andrew, Galen / Choquette-Choo, Christopher A. / Kairouz, Peter / McMahan, H. Brendan / Rosenstock, Jesse / Zhang, Yuanbo

2023

Abstract: 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 ... ...

Abstract	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. Comment: ACL industry track; v2 updating SecAgg details
Keywords	Computer Science - Machine Learning ; Computer Science - Cryptography and Security
Subject code	303
Publishing date	2023-05-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Learning to Generate Image Embeddings with User-level Differential Privacy

Xu, Zheng / Collins, Maxwell / Wang, Yuxiao / Panait, Liviu / Oh, Sewoong / Augenstein, Sean / Liu, Ting / Schroff, Florian / McMahan, H. Brendan

2022

Abstract: Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using ... ...

Abstract	Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in the datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of $\epsilon<4$ while controlling the utility drop within 5%, when millions of users can participate in training. Comment: CVPR camera ready. Addressed reviewer comments. Switched from add-or-remove-one DP to substitute-one DP
Keywords	Computer Science - Machine Learning ; Computer Science - Cryptography and Security ; Computer Science - Computer Vision and Pattern Recognition
Subject code	004 ; 006
Publishing date	2022-11-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Can You Really Backdoor Federated Learning?

Sun, Ziteng / Kairouz, Peter / Suresh, Ananda Theertha / McMahan, H. Brendan

2019

Abstract: The decentralized nature of federated learning makes detecting and defending against adversarial attacks a challenging task. This paper focuses on backdoor attacks in the federated learning setting, where the goal of the adversary is to reduce the ... ...

Abstract	The decentralized nature of federated learning makes detecting and defending against adversarial attacks a challenging task. This paper focuses on backdoor attacks in the federated learning setting, where the goal of the adversary is to reduce the performance of the model on targeted tasks while maintaining good performance on the main task. Unlike existing works, we allow non-malicious clients to have correctly labeled samples from the targeted tasks. We conduct a comprehensive study of backdoor attacks and defenses for the EMNIST dataset, a real-life, user-partitioned, and non-iid dataset. We observe that in the absence of defenses, the performance of the attack largely depends on the fraction of adversaries present and the "complexity'' of the targeted task. Moreover, we show that norm clipping and "weak'' differential privacy mitigate the attacks without hurting the overall performance. We have implemented the attacks and defenses in TensorFlow Federated (TFF), a TensorFlow framework for federated learning. In open-sourcing our code, our goal is to encourage researchers to contribute new attacks and defenses and evaluate them on standard federated datasets. Comment: To appear at the 2nd International Workshop on Federated Learning for Data Privacy and Confidentiality at NeurIPS 2019
Keywords	Computer Science - Machine Learning ; Computer Science - Cryptography and Security ; Statistics - Machine Learning
Subject code	006
Publishing date	2019-11-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Protective Efficacy of Rhesus Adenovirus COVID-19 Vaccines against Mouse-Adapted SARS-CoV-2.

Tostanoski, Lisa H / Gralinski, Lisa E / Martinez, David R / Schaefer, Alexandra / Mahrokhian, Shant H / Li, Zhenfeng / Nampanya, Felix / Wan, Huahua / Yu, Jingyou / Chang, Aiquan / Liu, Jinyan / McMahan, Katherine / Ventura, John D / Dinnon, Kenneth H / Leist, Sarah R / Baric, Ralph S / Barouch, Dan H

Journal of virology

2021 Volume 95, Issue 23, Page(s) e0097421

Abstract: The global COVID-19 pandemic has sparked intense interest in the rapid development of vaccines as well as animal models to evaluate vaccine candidates and to define immune correlates of protection. We recently reported a mouse-adapted SARS-CoV-2 virus ... ...

Abstract	The global COVID-19 pandemic has sparked intense interest in the rapid development of vaccines as well as animal models to evaluate vaccine candidates and to define immune correlates of protection. We recently reported a mouse-adapted SARS-CoV-2 virus strain (MA10) with the potential to infect wild-type laboratory mice, driving high levels of viral replication in respiratory tract tissues as well as severe clinical and respiratory symptoms, aspects of COVID-19 disease in humans that are important to capture in model systems. We evaluated the immunogenicity and protective efficacy of novel rhesus adenovirus serotype 52 (RhAd52) vaccines against MA10 challenge in mice. Baseline seroprevalence is lower for rhesus adenovirus vectors than for human or chimpanzee adenovirus vectors, making these vectors attractive candidates for vaccine development. We observed that RhAd52 vaccines elicited robust binding and neutralizing antibody titers, which inversely correlated with viral replication after challenge. These data support the development of RhAd52 vaccines and the use of the MA10 challenge virus to screen novel vaccine candidates and to study the immunologic mechanisms that underscore protection from SARS-CoV-2 challenge in wild-type mice.
MeSH term(s)	Adenoviridae Infections/immunology ; Adenovirus Vaccines/immunology ; Adenoviruses, Simian/immunology ; Animals ; Antibodies, Neutralizing/immunology ; Antibodies, Viral/immunology ; COVID-19/immunology ; COVID-19/prevention & control ; COVID-19 Vaccines/immunology ; Disease Models, Animal ; Female ; Humans ; Immunogenicity, Vaccine ; Macaca mulatta/virology ; Mice ; Mice, Inbred BALB C ; Pandemics/prevention & control ; SARS-CoV-2/immunology ; SARS-CoV-2/pathogenicity ; Vaccination
Chemical Substances	Adenovirus Vaccines ; Antibodies, Neutralizing ; Antibodies, Viral ; COVID-19 Vaccines
Language	English
Publishing date	2021-09-15
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	80174-4
ISSN	1098-5514 ; 0022-538X
ISSN (online)	1098-5514
ISSN	0022-538X
DOI	10.1128/JVI.00974-21
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