Consider two data providers, each maintaining private records of different feature sets about
common entities. They aim to learn a linear model jointly in a federated setting, namely, data …

Federated learning (FL) has been proposed to allow collaborative training of machine
learning (ML) models among multiple parties to keep their data private and only model …

We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical
federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized …

Federated Learning (FL) enables a large number of users to jointly learn a shared machine
learning (ML) model, coordinated by a centralized server, where the data is distributed …

In large-scale statistical learning, data collection and model fitting are moving increasingly
toward peripheral devices---phones, watches, fitness trackers---away from centralized data …

Machine learning benefits from large training datasets, which may not always be possible to
collect by any single entity, especially when using privacy-sensitive data. In many contexts …

In this paper, we address the problem of privacy-preserving distributed learning and the
evaluation of machine-learning models by analyzing it in the widespread MapReduce …

Federated learning is a recent advance in privacy protection. In this context, a trusted curator
aggregates parameters optimized in decentralized fashion by multiple clients. The resulting …

Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables
multiple parties to jointly re-train a shared model without sharing their data with any other …

In a federated learning scenario where multiple parties jointly learn a model from their
respective data, there exist two conflicting goals for the choice of appropriate algorithms. On …