6.3k citations · 20.9k across the 54 of their papers we have counts for
6 papers · 1 filter
Practical Secure Aggregation for Federated Learning on User-Held Data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter +6
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the…
Distributed Mean Estimation with Limited Communication
Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar +1
Motivated by the need for distributed learning and optimization algorithms with low communication cost, we study communication efficient algorithms for distributed mean estimation.…
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
Jakub Konečný, H. Brendan McMahan, Daniel Ramage +1
We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an ext…
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu +3
Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients…
Deep Learning with Differential Privacy
Martín Abadi, Andy Chu, Ian Goodfellow +4
Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative dat…
Communication-Efficient Learning of Deep Networks from Decentralized Data
H. Brendan McMahan, Eider Moore, Daniel Ramage +2
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language model…