65 citations · 177 across the 68 of their papers we have counts for
5 papers · 1 filter
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…
Private Aggregation from Fewer Anonymous Messages
Badih Ghazi, Pasin Manurangsi, Rasmus Pagh +1
Consider the setup where parties are each given a number and the goal is to compute the sum in a secure fashion and with as little communica…
On the Power of Multiple Anonymous Messages
Badih Ghazi, Noah Golowich, Ravi Kumar +2
An exciting new development in differential privacy is the shuffled model, in which an anonymous channel enables non-interactive, differentially private protocols with error much s…
Scalable and Differentially Private Distributed Aggregation in the Shuffled Model
Badih Ghazi, Rasmus Pagh, Ameya Velingker
Federated learning promises to make machine learning feasible on distributed, private datasets by implementing gradient descent using secure aggregation methods. The idea is to com…
Recursive Sketches for Modular Deep Learning
Badih Ghazi, Rina Panigrahy, Joshua R. Wang
We present a mechanism to compute a sketch (succinct summary) of how a complex modular deep network processes its inputs. The sketch summarizes essential information about the inpu…