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Wyniki wyszukiwania dla: federated learning
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Towards Scalable Simulation of Federated Learning
PublikacjaFederated learning (FL) allows to train models on decentralized data while maintaining data privacy, which unlocks the availability of large and diverse datasets for many practical applications. The ongoing development of aggregation algorithms, distribution architectures and software implementations aims for enabling federated setups employing thousands of distributed devices, selected from millions. Since the availability of...
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A Mammography Data Management Application for Federated Learning
PublikacjaThis study aimed to develop and assess an application designed to enhance the management of a local client database consisting of mammographic images with a focus on ensuring that images are suitably and uniformly prepared for federated learning applications. The application supports a comprehensive approach, starting with a versatile image-loading function that supports DICOM files from various medical imaging devices and settings....
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Federated Learning in Healthcare Industry: Mammography Case Study
PublikacjaThe paper focuses on the role of federated learning in a healthcare environment. The experimental setup involved different healthcare providers, each with their datasets. A comparison was made between training a deep learning model using traditional methods, where all the data is stored in one place, and using federated learning, where the data is distributed among the workers. The experiment aimed to identify possible challenges...
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Data Domain Adaptation in Federated Learning in the Breast Mammography Image Classification Problem
PublikacjaWe are increasingly striving to introduce modern artificial intelligence techniques in medicine and elevate medical care, catering to both patients and specialists. An essential aspect that warrants concurrent development is the protection of personal data, especially with technology's advancement, along with addressing data disparities to ensure model efficacy. This study assesses various domain adaptation techniques and federated...
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Mitigation of Fake Data Content Poisoning Attacks in NDN via Blockchain
PublikacjaAbstract—Information-centric networks struggle with content poisoning attacks (CPAs), especially their stronger form called Fake Data CPA, in which an intruder publisher uploads content signed with stolen credentials. Following an existing graphinfection based approach leveraging the constrained time when stolen credentials are useful, we design a blockchain-based mitigation scheme for Named Data Networking architectures. We postulate...
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Bridging challenges of clinical decision support systems with a semantic approach. A case study on breast cancer
PublikacjaThe integration of Clinical Decision Support Systems (CDSS) in nowadays clinical environments has not been fully achieved yet. Although numerous approaches and technologies have been proposed since 1960, there are still open gaps that need to be bridged. In this work we present advances from the established state of the art, overcoming some of the most notorious reported difficulties in: (i) automating CDSS, (ii) clinical workflow...
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Long Distance Geographically Distributed InfiniBand Based Computing
PublikacjaCollaboration between multiple computing centres, referred as federated computing is becom- ing important pillar of High Performance Computing (HPC) and will be one of its key components in the future. To test technical possibilities of future collaboration using 100 Gb optic fiber link (Connection was 900 km in length with 9 ms RTT time) we prepared two scenarios of operation. In the first one, Interdisciplinary Centre for Mathematical...