31 citations · 42 across the 9 of their papers we have counts for
20 papers
Model Card for OpenAI Privacy Filter
Charles de Bourcy, Sahra Ghalebikesabi, Avi Schwarzschild +22
OpenAI Privacy Filter is a compact, bidirectional token-classification model for detecting and redacting personally identifiable information (PII) and secrets in unstructured text.…
Memory-Efficient Backpropagation for Fine-Tuning LLMs on Resource-Constrained Mobile Devices
Congzheng Song, Xinyu Tang
Fine-tuning large language models (LLMs) with backpropagation\textemdash even for a subset of parameters such as LoRA\textemdash can be much more memory-consuming than inference an…
Private Federated Learning In Real World Application -- A Case Study
An Ji, Bortik Bandyopadhyay, Congzheng Song +7
This paper presents an implementation of machine learning model training using private federated learning (PFL) on edge devices. We introduce a novel framework that uses PFL to add…
pfl-research: simulation framework for accelerating research in Private Federated Learning
Filip Granqvist, Congzheng Song, Áine Cahill +7
Federated learning (FL) is an emerging machine learning (ML) training paradigm where clients own their data and collaborate to train a global model, without revealing any data to t…
Momentum Approximation in Asynchronous Private Federated Learning
Tao Yu, Congzheng Song, Jianyu Wang +1
Asynchronous protocols have been shown to improve the scalability of federated learning (FL) with a massive number of clients. Meanwhile, momentum-based methods can achieve the bes…
Population Expansion for Training Language Models with Private Federated Learning
Tatsuki Koga, Congzheng Song, Martin Pelikan +1
Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee. With a large pop…