390 citations · 543 across the 9 of their papers we have counts for
5 papers · 1 filter
Federated Discrete Denoising Diffusion Model for Molecular Generation with OpenFL
Kevin Ta, Patrick Foley, Mattson Thieme +2
Generating unique molecules with biochemically desired properties to serve as viable drug candidates is a difficult task that requires specialized domain expertise. In recent years…
Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry
Panyi Dong, Zhiyu Quan, Brandon Edwards +5
The report demonstrates the benefits (in terms of improved claims loss modeling) of harnessing the value of Federated Learning (FL) to learn a single model across multiple insuranc…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery
Odhran O'Donoghue, Paul Duckworth, Giuseppe Ughi +10
Human medical data can be challenging to obtain due to data privacy concerns, difficulties conducting certain types of experiments, or prohibitive associated costs. In many setting…
OpenFL: An open-source framework for Federated Learning
G Anthony Reina, Alexey Gruzdev, Patrick Foley +15
Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient rec…