Search Results for author: Dimitris Stripelis

Found 12 papers, 0 papers with code

Accelerating Federated Learning in Heterogeneous Data and Computational Environments

no code implementations25 Aug 2020 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 privacy reasons.

Federated Learning

Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings

no code implementations4 Feb 2021 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 privacy reasons.

BIG-bench Machine Learning Federated Learning

Membership Inference Attacks on Deep Regression Models for Neuroimaging

no code implementations6 May 2021 Umang Gupta, Dimitris Stripelis, Pradeep K. Lam, Paul M. Thompson, José Luis Ambite, Greg Ver Steeg

In particular, we show that it is possible to infer if a sample was used to train the model given only access to the model prediction (black-box) or access to the model itself (white-box) and some leaked samples from the training data distribution.

Federated Learning regression

Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption

no code implementations7 Aug 2021 Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location.

Benchmarking Federated Learning

Federated Named Entity Recognition

no code implementations28 Mar 2022 Joel Mathew, Dimitris Stripelis, José Luis Ambite

We present an analysis of the performance of Federated Learning in a paradigmatic natural-language processing task: Named-Entity Recognition (NER).

Federated Learning named-entity-recognition +2

Federated Progressive Sparsification (Purge, Merge, Tune)+

no code implementations26 Apr 2022 Dimitris Stripelis, Umang Gupta, Greg Ver Steeg, Jose Luis Ambite

Second, the models are incrementally constrained to a smaller set of parameters, which facilitates alignment/merging of the local models and improved learning performance at high sparsification rates.

Secure & Private Federated Neuroimaging

no code implementations11 May 2022 Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil Dhinagar, Tanmay Ghai, Rafael Chrysovalantis Anastasiou, Armaghan Asghar, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

Each site trains the neural network over its private data for some time, then shares the neural network parameters (i. e., weights, gradients) with a Federation Controller, which in turn aggregates the local models, sends the resulting community model back to each site, and the process repeats.

Federated Learning

Towards Sparsified Federated Neuroimaging Models via Weight Pruning

no code implementations24 Aug 2022 Dimitris Stripelis, Umang Gupta, Nikhil Dhinagar, Greg Ver Steeg, Paul Thompson, José Luis Ambite

In our experiments in centralized and federated settings on the brain age prediction task (estimating a person's age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions.

Federated Learning

Federated Learning over Harmonized Data Silos

no code implementations15 May 2023 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 sharing data.

Data Integration Federated Learning +2

MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows

no code implementations1 Nov 2023 Dimitris Stripelis, Chrysovalantis Anastasiou, Patrick Toral, Armaghan Asghar, Jose Luis Ambite

The controller is responsible for managing the execution of FL workflows across learners and the learners for training and evaluating federated models over their private datasets.

Federated Learning Scheduling

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