30 citations · 42 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
Training Large-Vocabulary Neural Language Models by Private Federated Learning for Resource-Constrained Devices
Mingbin Xu, Congzheng Song, Ye Tian +10
Federated Learning (FL) is a technique to train models using data distributed across devices. Differential Privacy (DP) provides a formal privacy guarantee for sensitive data. Our…
FLAIR: Federated Learning Annotated Image Repository
Congzheng Song, Filip Granqvist, Kunal Talwar
Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devic…
Enforcing fairness in private federated learning via the modified method of differential multipliers
Borja Rodríguez-Gálvez, Filip Granqvist, Rogier van Dalen +1
Federated learning with differential privacy, or private federated learning, provides a strategy to train machine learning models while respecting users' privacy. However, differen…
Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
Matthias Paulik, Matt Seigel, Henry Mason +19
We describe the design of our federated task processing system. Originally, the system was created to support two specific federated tasks: evaluation and tuning of on-device ML sy…