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View article: Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up
Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up Open
The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of g…
View article: Leveraging Open-Source Large Language Models to Identify Undiagnosed Patients with Rare Genetic Aortopathies
Leveraging Open-Source Large Language Models to Identify Undiagnosed Patients with Rare Genetic Aortopathies Open
Rare genetic aortopathies are frequently undiagnosed due to phenotypic heterogeneity, and delayed diagnosis can lead to fatal cardiac outcomes. While genetic testing can enable early proactive interventions, it relies on primary care physi…
View article: RADAR-Radio Afterglow Detection and AI-driven Response: A Federated Framework for Gravitational Wave Event Follow-Up
RADAR-Radio Afterglow Detection and AI-driven Response: A Federated Framework for Gravitational Wave Event Follow-Up Open
The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of g…
View article: CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning Open
Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and security of a client's data without exchanging it. However, ensu…
View article: FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud
FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud Open
Federated learning (FL) is a distributed machine learning (ML) approach that allows multiple clients to collaboratively train ML models without exchanging original training data, offering a solution that is particularly valuable in sensiti…
View article: Genome-Wide Assessment of Pleiotropy Across >1000 Traits from Global Biobanks
Genome-Wide Assessment of Pleiotropy Across >1000 Traits from Global Biobanks Open
Large-scale genetic association studies have identified thousands of trait-associated risk loci, establishing the polygenic basis for common complex traits and diseases. Although prior studies suggest that many trait-associated loci are pl…
View article: Pathology Image Compression with Pre-trained Autoencoders
Pathology Image Compression with Pre-trained Autoencoders Open
The growing volume of high-resolution Whole Slide Images in digital histopathology poses significant storage, transmission, and computational efficiency challenges. Standard compression methods, such as JPEG, reduce file sizes but often fa…
View article: EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants Open
Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that require reasoning, problem-solving, and decision-making. Their…
View article: Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx
Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx Open
Facilitating large-scale, cross-institutional collaboration in biomedical machine learning (ML) projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health…
View article: Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning Open
Self-supervised learning (SSL) methods have emerged as strong visual representation learners by training an image encoder to maximize similarity between features of different views of the same image. To perform this view-invariance task, c…
View article: FedSpaLLM: Federated Pruning of Large Language Models
FedSpaLLM: Federated Pruning of Large Language Models Open
Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands. Pruning can reduce model size, yet existing methods assume public access to calibration da…
View article: Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System
Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System Open
The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant …
View article: Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework Open
Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy. In today's landscape, where most data is proprietary, confidential, and distributed, FL has become a pr…
View article: Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2 Open
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the parameters of the locally trained models. In this paper, we …
View article: Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx
Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx Open
Facilitating large-scale, cross-institutional collaboration in biomedical machine learning projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health info…
View article: FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler Open
Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., healthcare, financial, as well as scientific projects that lack a…
View article: APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service Open
Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accel…
View article: ViCTer: A semi-supervised video character tracker
ViCTer: A semi-supervised video character tracker Open
Video character tracking problem refers to tracking certain characters of interest in the video and returning the appearing time slots for those characters. Solutions to this problem can be applied in various video-analysis-related areas, …