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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Updated: Aug 26, 2025

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
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OpenFL: the open federated learning library.

Patrick Foley1, Micah J Sheller1, Brandon Edwards1

  • 1Intel Corporation, Santa Clara, CA 95052, United States of America.

Physics in Medicine and Biology
|October 5, 2022
PubMed
Summary
This summary is machine-generated.

Open Federated Learning (OpenFL) enables collaborative machine learning without data sharing. This open-source framework facilitates secure, scalable model training across diverse applications, including healthcare.

Keywords:
deep learningfederated learningmachine learningopen-sourceprivacysecurity

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Federated learning (FL) allows collaborative model training without sharing sensitive data.
  • Organizations need secure and scalable solutions for privacy-preserving machine learning.

Purpose of the Study:

  • Introduce the Open Federated Learning (OpenFL) framework.
  • Demonstrate OpenFL's applicability in real-world scenarios and its potential beyond healthcare.

Main Methods:

  • OpenFL is an open-source Python framework supporting TensorFlow and PyTorch.
  • It facilitates the migration of centralized ML models to a federated training pipeline.
  • Recommendations for securing federations using trusted execution environments are provided.

Main Results:

  • OpenFL enables data-private collaborative learning for machine learning (ML) and deep learning (DL).
  • The framework supports scalable, trusted execution for production environments.
  • Successful real-world healthcare federations using OpenFL are described.

Conclusions:

  • OpenFL is a versatile tool for federated learning applicable across various domains.
  • It prioritizes scalability, security, and ease of migration for ML models.
  • The library is readily available for research and production adoption.