6.3k citations · 20.9k across the 54 of their papers we have counts for
10 papers · 1 filter
Can You Really Backdoor Federated Learning?
Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh +1
The decentralized nature of federated learning makes detecting and defending against adversarial attacks a challenging task. This paper focuses on backdoor attacks in the federated…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
Keith Bonawitz, Fariborz Salehi, Jakub Konečný +2
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do mac…
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…
Differentially Private Learning with Adaptive Clipping
Galen Andrew, Om Thakkar, H. Brendan McMahan +1
Existing approaches for training neural networks with user-level differential privacy (e.g., DP Federated Averaging) in federated learning (FL) settings involve bounding the contri…
Semi-Cyclic Stochastic Gradient Descent
Hubert Eichner, Tomer Koren, H. Brendan McMahan +2
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-sp…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
Towards Federated Learning at Scale: System Design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp +11
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for F…
Federated Heavy Hitters Discovery with Differential Privacy
Wennan Zhu, Peter Kairouz, Brendan McMahan +2
The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but can incur substantial privacy risks if no…
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Sebastian Caldas, Jakub Konečny, H. Brendan McMahan +1
Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation. To address this issue,…