1.1k citations · 1.1k across the 7 of their papers we have counts for
5 papers · 1 filter
Learning from straggler clients in federated learning
Andrew Hard, Antonious M. Girgis, Ehsan Amid +4
How well do existing federated learning algorithms learn from client devices that return model updates with a significant time delay? Is it even possible to learn effectively from…
Learning to Generate Image Embeddings with User-level Differential Privacy
Zheng Xu, Maxwell Collins, Yuxiao Wang +6
Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existi…
Mixed Federated Learning: Joint Decentralized and Centralized Learning
Sean Augenstein, Andrew Hard, Lin Ning +4
Federated learning (FL) enables learning from decentralized privacy-sensitive data, with computations on raw data confined to take place at edge clients. This paper introduces mixe…
Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift
Sean Augenstein, Andrew Hard, Kurt Partridge +1
With privacy as a motivation, Federated Learning (FL) is an increasingly used paradigm where learning takes place collectively on edge devices, each with a cache of user-generated…
Generative Models for Effective ML on Private, Decentralized Datasets
Sean Augenstein, H. Brendan McMahan, Daniel Ramage +5
To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact. Manual inspection of ra…