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Found 7 papers, 3 papers with code
Learning from straggler clients in federated learning
How well do existing federated learning algorithms learn from client devices that return model updates with a significant time delay?
Learning to Generate Image Embeddings with User-level Differential Privacy
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.
Mixed Federated Learning: Joint Decentralized and Centralized Learning
For example, additional datacenter data can be leveraged to jointly learn from centralized (datacenter) and decentralized (federated) training data and better match an expected inference data distribution.
Production federated keyword spotting via distillation, filtering, and joint federated-centralized training
We trained a keyword spotting model using federated learning on real user devices and observed significant improvements when the model was deployed for inference on phones.
Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift
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 training examples that remain resident on the local device.
Generative Models for Effective ML on Private, Decentralized Datasets
To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact.
Federated Learning for Mobile Keyboard Prediction
We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones.
