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Found 9 papers, 6 papers with code
Confidential Federated Computations
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data.
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets
In this work, we focus on federated training of dual encoding models on decentralized data composed of many small, non-IID (independent and identically distributed) client datasets.
A Field Guide to Federated Optimization
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Federated Evaluation of On-device Personalization
Federated learning is a distributed, on-device computation framework that enables training global models without exporting sensitive user data to servers.
Semi-Cyclic Stochastic Gradient Descent
We consider convex SGD updates with a block-cyclic structure, i. e. where each cycle consists of a small number of blocks, each with many samples from a possibly different, block-specific, distribution.
Towards Federated Learning at Scale: System Design
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.
Applied Federated Learning: Improving Google Keyboard Query Suggestions
Federated learning is a distributed form of machine learning where both the training data and model training are decentralized.
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.
