As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

Due to the increasing privacy concerns and data regulations, training data have been
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …

This article presents and characterizes an Open Application Repository for Federated
Learning (OARF), a benchmark suite for federated machine learning systems. Previously …

Due to the increase in data regulations amid rising privacy concerns, the machine learning
(ML) community has proposed a novel distributed training paradigm called federated …

Federated learning is a multi-party distributed machine learning system that allows each
participant to complete the training task without their data out of the locality. The real …

Federated Learning (FL) has been widely accepted as the solution for privacy-preserving
machine learning without collecting raw data. While new technologies proposed in the past …

This paper investigates a class of privacy-enhancing decentralized federated learning (DFL)
algorithms. The majority of current DFL algorithms rely on the premise that obtaining …

Federated Learning (FL) is an emerging learning paradigm that enables collaborative model
training, across multiple devices using decentralized data, allowing each device to keep the …

Federated learning (FL) has been proposed as a machine learning approach to
collaboratively learn a shared prediction model. Although, during FL training, only a subset …

In the last few years, consumers have become more than ever aware of their data
sovereignty and privacy. In addition, companies need more and more to collaborate but are …