Secure aggregation is concerned with the task of securely uploading the inputs of multiple
users to an aggregation server without letting the server know the inputs beyond their …

We consider the federated submodel learning (FSL) problem and propose an approach
where clients are able to update the central model information theoretically privately. Our …

A collection of $ K $ random variables are called $(K, n) $-MDS if any $ n $ of the $ K $
variables are independent and determine all remaining variables. In the MDS variable …

We consider the federated submodel learning (FSL) problem in a distributed storage system.
In the FSL framework, the full learning model at the server side is divided into multiple …

In secure summation, K users, each holds an input, wish to compute the sum of the inputs at
a server without revealing any information about all the inputs even if the server may collude …

The growing privacy concerns in distributed learning have led to the widespread adoption of
secure aggregation techniques in distributed machine learning systems, such as federated …

Secure aggregation usually aims at securely computing the sum of the inputs from $ K $
users at a server. Noticing that the sum might inevitably reveal information about the inputs …

Secure aggregation is concerned with the task of securely uploading the inputs associated
with multiple users to an aggregation server without revealing the user inputs to the server …

Consider a scenario involving multiple users and a fusion center. Each user possesses a
sequence of bits and can communicate with the fusion center through a one-way public …

The secure summation problem is studied with user selection and collusion, where a server
may select any U out of K users and compute the sum of the inputs from the selected users …