no code implementations • 1 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.
no code implementations • 15 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.
no code implementations • 11 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.
no code implementations • 2 May 2022 • Dimitris Stripelis, Marcin Abram, Jose Luis Ambite
Here, we focus on the latter, the susceptibility of federated learning to various data corruption attacks.
no code implementations • 26 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.
no code implementations • 7 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.
no code implementations • 16 Feb 2021 • Dimitris Stripelis, Jose Luis Ambite, Pradeep Lam, Paul Thompson
Federated Learning is a promising approach to learn a joint model over data silos.
no code implementations • 4 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.
no code implementations • 25 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.
no code implementations • 19 Nov 2018 • Shobeir Fakhraei, Joel Mathew, Jose Luis Ambite
An important task in this process is entity normalization, which consists of mapping noisy entity mentions in text to canonical entities in well-known reference sets.
no code implementations • 16 Jan 2016 • Mohsen Taheriyan, Craig A. Knoblock, Pedro Szekely, Jose Luis Ambite
This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology.