Search Results for author: Robert Sim

Found 14 papers, 1 papers with code

Federated Multilingual Models for Medical Transcript Analysis

no code implementations4 Nov 2022 Andre Manoel, Mirian Hipolito Garcia, Tal Baumel, Shize Su, Jialei Chen, Dan Miller, Danny Karmon, Robert Sim, Dimitrios Dimitriadis

Federated Learning (FL) is a novel machine learning approach that allows the model trainer to access more data samples, by training the model across multiple decentralized data sources, while data access constraints are in place.

Federated Learning

Privacy Leakage in Text Classification: A Data Extraction Approach

no code implementations9 Jun 2022 Adel Elmahdy, Huseyin A. Inan, Robert Sim

Recent work has demonstrated the successful extraction of training data from generative language models.

Classification Memorization +2

Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning

no code implementations27 Apr 2022 Yae Jee Cho, Andre Manoel, Gauri Joshi, Robert Sim, Dimitrios Dimitriadis

In this work, we propose a novel ensemble knowledge transfer method named Fed-ET in which small models (different in architecture) are trained on clients, and used to train a larger model at the server.

Ensemble Learning Federated Learning +1

FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations

1 code implementation25 Mar 2022 Mirian Hipolito Garcia, Andre Manoel, Daniel Madrigal Diaz, FatemehSadat Mireshghallah, Robert Sim, Dimitrios Dimitriadis

We compare the platform with other state-of-the-art platforms and describe available features of FLUTE for experimentation in core areas of active research, such as optimization, privacy, and scalability.

Federated Learning Quantization +2

Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels

no code implementations NAACL 2021 FatemehSadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor R{\"u}hle, Taylor Berg-Kirkpatrick, Robert Sim

In this work, we introduce two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy through (1) the use of a discriminator and (2) the inclusion of a novel triplet-loss term.

Memorization Privacy Preserving

On Privacy and Confidentiality of Communications in Organizational Graphs

no code implementations27 May 2021 Masoumeh Shafieinejad, Huseyin Inan, Marcello Hasegawa, Robert Sim

We propose a model that captures the correlation in the social network graph, and incorporates this correlation in the privacy calculations through Pufferfish privacy principles.

Language Modelling

Privacy Regularization: Joint Privacy-Utility Optimization in Language Models

no code implementations12 Mar 2021 FatemehSadat Mireshghallah, Huseyin A. Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim

In this work, we introduce two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy through (1) the use of a discriminator and (2) the inclusion of a triplet-loss term.

Memorization Privacy Preserving

Training Data Leakage Analysis in Language Models

no code implementations14 Jan 2021 Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim

It has been demonstrated that strong performance of language models comes along with the ability to memorize rare training samples, which poses serious privacy threats in case the model is trained on confidential user content.

Conversations with Documents. An Exploration of Document-Centered Assistance

no code implementations27 Jan 2020 Maartje ter Hoeve, Robert Sim, Elnaz Nouri, Adam Fourney, Maarten de Rijke, Ryen W. White

Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario.

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