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 …
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 …
Scenario-based Adaptations of Differential Privacy: A Technical Survey
Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …
privacy preservation. It has had great success in scenarios of local data privacy and …
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 …
Differentially private byzantine-robust federated learning
Federated learning is a collaborative machine learning framework where a global model is
trained by different organizations under the privacy restrictions. Promising as it is, privacy …
trained by different organizations under the privacy restrictions. Promising as it is, privacy …
Flame: Differentially private federated learning in the shuffle model
Federated Learning (FL) is a promising machine learning paradigm that enables the
analyzer to train a model without collecting users' raw data. To ensure users' privacy …
analyzer to train a model without collecting users' raw data. To ensure users' privacy …
On the power of multiple anonymous messages: Frequency estimation and selection in the shuffle model of differential privacy
It is well-known that general secure multi-party computation can in principle be applied to
implement differentially private mechanisms over distributed data with utility matching the …
implement differentially private mechanisms over distributed data with utility matching the …
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 …
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 …