Search results for: FEDERATED LEARNING

• Towards Scalable Simulation of Federated Learning

  Publication

  • T. Kołodziej
  • P. Rościszewski

  - Communications in Computer and Information Science - Year 2021

  Federated 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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• Federated Learning in Healthcare Industry: Mammography Case Study

  Publication

  • K. Zieliński
  • N. Kowalczyk
  • T. Kocejko
  • M. Mazur-Milecka
  • T. Neumann
  • J. Rumiński

  - Year 2023

  The 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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• Engineering Federated Information (Database) Systems

  Conferences