390 citations · 543 across the 9 of their papers we have counts for
9 papers
Supercharging Federated Intelligence Retrieval
Dimitris Stripelis, Patrick Foley, Mohammad Naseri +4
RAG typically assumes centralized access to documents, which breaks down when knowledge is distributed across private data silos. We propose a secure Federated RAG system built usi…
The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning
Akis Linardos, Sarthak Pati, Ujjwal Baid +25
We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in m…
A Technical Policy Blueprint for Trustworthy Decentralized AI
Hasan Kassem, Orion Banks, Omar Benjelloun +20
Decentralized AI systems, such as federated learning, can play a critical role in further unlocking AI asset marketplaces (e.g., healthcare data marketplaces) thanks to increased a…
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…
The Federated Tumor Segmentation (FeTS) Challenge
Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29
This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…
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…