42 citations · 59 across the 15 of their papers we have counts for
9 papers · 1 filter
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…
Towards Sparsified Federated Neuroimaging Models via Weight Pruning
Dimitris Stripelis, Umang Gupta, Nikhil Dhinagar +3
Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model prunin…
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…
Secure & Private Federated Neuroimaging
Dimitris Stripelis, Umang Gupta, Hamza Saleem +9
The amount of biomedical data continues to grow rapidly. However, collecting data from multiple sites for joint analysis remains challenging due to security, privacy, and regulator…
Federated Progressive Sparsification (Purge, Merge, Tune)+
Dimitris Stripelis, Umang Gupta, Greg Ver Steeg +1
To improve federated training of neural networks, we develop FedSparsify, a sparsification strategy based on progressive weight magnitude pruning. Our method has several benefits.…
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…
Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings
Dimitris Stripelis, Jose Luis Ambite
There are situations where data relevant to machine learning problems are distributed across multiple locations that cannot share the data due to regulatory, competitiveness, or pr…
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…