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Related Concept Videos

Introduction to Learning01:18

Introduction to Learning

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.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Scale-Up Processes01:14

Scale-Up Processes

The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...

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Related Experiment Videos

Implementing a Governance Framework for Federated Learning.

Ana Grönke1, Mehrshad Jaberansary2, Oussama Zoubia2

  • 1Medical Data Integration Center, University of Cologne.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Federated learning in healthcare AI is growing, but hospital use is limited. This study adapts a governance framework to enable AI-driven secondary use of clinical data in federated settings.

Keywords:
AIFederated learningGDPRGovernancePADME

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Healthcare Informatics
  • Machine Learning

Background:

  • Federated learning (FL) is increasingly vital for multi-institutional AI research in healthcare.
  • However, its adoption within highly secure hospital environments faces significant limitations.
  • The secondary use of clinical routine data for AI requires robust frameworks.

Purpose of the Study:

  • To evaluate a proposed governance framework for federated learning.
  • To explore adaptations for facilitating AI-driven secondary use of clinical data in federated hospital settings.

Main Methods:

  • Evaluation of an existing federated learning governance framework.
  • Analysis of requirements for secondary use of clinical routine data.
  • Adaptation strategies for the governance framework in a federated hospital context.

Main Results:

  • The study identified key challenges in applying federated learning within secure hospital domains.
  • Proposed adaptations to the governance framework were explored to address these challenges.
  • The framework's potential to facilitate AI-driven secondary data use was assessed.

Conclusions:

  • Federated learning governance frameworks require specific adaptations for secure hospital data use.
  • The adapted framework shows promise for enabling AI-driven secondary use of clinical data in federated settings.
  • Further development is needed to fully realize federated learning's potential in hospitals.