3 papers
cs.LG2024
pfl-research: simulation framework for accelerating research in Private Federated Learning
Filip Granqvist, Congzheng Song, Ãine 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…
cs.CR2024
Samplable Anonymous Aggregation for Private Federated Data Analysis
Kunal Talwar, Shan Wang, Audra McMillan +34
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private…
cs.LG2024
Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials
Jonathan Scott, Ãine Cahill
In practice, training using federated learning can be orders of magnitude slower than standard centralized training. This severely limits the amount of experimentation and tuning t…