Advances and open problems in federated learning
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …
devices or whole organizations) collaboratively train a model under the orchestration of a …
The distributed discrete gaussian mechanism for federated learning with secure aggregation
We consider training models on private data that are distributed across user devices. To
ensure privacy, we add on-device noise and use secure aggregation so that only the noisy …
ensure privacy, we add on-device noise and use secure aggregation so that only the noisy …
Shuffled model of differential privacy in federated learning
We consider a distributed empirical risk minimization (ERM) optimization problem with
communication efficiency and privacy requirements, motivated by the federated learning …
communication efficiency and privacy requirements, motivated by the federated learning …
The fundamental price of secure aggregation in differentially private federated learning
We consider the problem of training a $ d $ dimensional model with distributed differential
privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees …
privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees …
Differential privacy in the shuffle model: A survey of separations
A Cheu - arXiv preprint arXiv:2107.11839, 2021 - arxiv.org
Differential privacy is often studied in one of two models. In the central model, a single
analyzer has the responsibility of performing a privacy-preserving computation on data. But …
analyzer has the responsibility of performing a privacy-preserving computation on data. But …
Privacy amplification via compression: Achieving the optimal privacy-accuracy-communication trade-off in distributed mean estimation
Privacy and communication constraints are two major bottlenecks in federated learning (FL)
and analytics (FA). We study the optimal accuracy of mean and frequency estimation …
and analytics (FA). We study the optimal accuracy of mean and frequency estimation …
User-level differentially private learning via correlated sampling
Most works in learning with differential privacy (DP) have focused on the setting where each
user has a single sample. In this work, we consider the setting where each user holds $ m …
user has a single sample. In this work, we consider the setting where each user holds $ m …
Shuffled model of federated learning: Privacy, accuracy and communication trade-offs
We consider a distributed empirical risk minimization (ERM) optimization problem with
communication efficiency and privacy requirements, motivated by the federated learning …
communication efficiency and privacy requirements, motivated by the federated learning …
Distributed, private, sparse histograms in the two-server model
We consider the computation of sparse,(ε, ϑ)-differentially private~(DP) histograms in the
two-server model of secure multi-party computation~(MPC), which has recently gained …
two-server model of secure multi-party computation~(MPC), which has recently gained …
Differentially private aggregation in the shuffle model: Almost central accuracy in almost a single message
The shuffle model of differential privacy has attracted attention in the literature due to it being
a middle ground between the well-studied central and local models. In this work, we study …
a middle ground between the well-studied central and local models. In this work, we study …