64 citations · 91 across the 11 of their papers we have counts for
7 papers · 1 filter
FedDAPL: Toward Client-Private Generalization in Federated Learning
Soroosh Safari Loaliyan, Jose-Luis Ambite, Paul M. Thompson +2
Federated Learning (FL) trains models locally at each research center or clinic and aggregates only model updates, making it a natural fit for medical imaging, where strict privacy…
MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows
Dimitris Stripelis, Chrysovalantis Anastasiou, Patrick Toral +2
A Federated Learning (FL) system typically consists of two core processing entities: the federation controller and the learners. The controller is responsible for managing the exec…
Federated Learning over Harmonized Data Silos
Dimitris Stripelis, Jose Luis Ambite
Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sha…
Performance Weighting for Robust Federated Learning Against Corrupted Sources
Dimitris Stripelis, Marcin Abram, Jose Luis Ambite
Federated Learning has emerged as a dominant computational paradigm for distributed machine learning. Its unique data privacy properties allow us to collaboratively train models wh…
Scaling Neuroscience Research using Federated Learning
Dimitris Stripelis, Jose Luis Ambite, Pradeep Lam +1
The amount of biomedical data continues to grow rapidly. However, the ability to analyze these data is limited due to privacy and regulatory concerns. Machine learning approaches t…
Accelerating Federated Learning in Heterogeneous Data and Computational Environments
Dimitris Stripelis, Jose Luis Ambite
There are situations where data relevant to a machine learning problem are distributed among multiple locations that cannot share the data due to regulatory, competitiveness, or pr…