139 citations · 410 across the 19 of their papers we have counts for
19 papers
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin A. Inan +6
We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the priv…
Project Florida: Federated Learning Made Easy
Daniel Madrigal Diaz, Andre Manoel, Jialei Chen +2
We present Project Florida, a system architecture and software development kit (SDK) enabling deployment of large-scale Federated Learning (FL) solutions across a heterogeneous dev…
TrojanPuzzle: Covertly Poisoning Code-Suggestion Models
Hojjat Aghakhani, Wei Dai, Andre Manoel +7
With tools like GitHub Copilot, automatic code suggestion is no longer a dream in software engineering. These tools, based on large language models, are typically trained on massiv…
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…
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…