22 citations · 89 across the 20 of their papers we have counts for
5 papers · 1 filter
Federated Multilingual Models for Medical Transcript Analysis
Andre Manoel, Mirian Hipolito Garcia, Tal Baumel +6
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 sou…
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Xiang Yue, Huseyin A. Inan, Xuechen Li +6
Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating syntheti…
Privacy Leakage in Text Classification: A Data Extraction Approach
Adel Elmahdy, Huseyin A. Inan, Robert Sim
Recent work has demonstrated the successful extraction of training data from generative language models. However, it is not evident whether such extraction is feasible in text clas…
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi +2
Federated learning (FL) enables edge-devices to collaboratively learn a model without disclosing their private data to a central aggregating server. Most existing FL algorithms req…
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Mirian Hipolito Garcia, Andre Manoel, Daniel Madrigal Diaz +3
In this paper we introduce "Federated Learning Utilities and Tools for Experimentation" (FLUTE), a high-performance open-source platform for federated learning research and offline…