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Distributed Loads01:19

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Distributed Loads: Problem Solving01:21

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Transformers in Distribution System01:27

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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...
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Issues And Trends In Healthcare Delivery System01:29

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Related Experiment Video

Updated: Aug 15, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems.

Ramin Firouzi1, Rahim Rahmani1

  • 1Department of Computer and Systems Sciences, Stockholm University, 16407 Stockholm, Sweden.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

This study introduces a new method for deploying federated learning (FL) in Open Radio Access Networks (O-RAN) using reinforcement learning (RL) for optimized client selection and resource allocation, improving 5G applications.

Keywords:
IoT, distributed intelligenceO-RANfederated learningfifth-generation mobile network (5G)reinforcement learning

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Area of Science:

  • Telecommunications Engineering
  • Artificial Intelligence
  • Distributed Systems

Background:

  • Federated learning (FL) offers decentralized, privacy-preserving model training but lacks effective deployment in Radio Access Networks (RAN).
  • Emerging RAN paradigms, like Open RAN (O-RAN), enable distributed intelligence with intelligent controllers, crucial for advanced mobile networks (5G/6G).

Purpose of the Study:

  • To propose and evaluate a methodology for deploying and optimizing federated learning tasks within O-RAN environments.
  • To enhance distributed intelligence for 5G applications by leveraging O-RAN's capabilities.

Main Methods:

  • Utilized reinforcement learning (RL) for intelligent client selection and resource allocation within O-RAN's RAN intelligent controllers (RIC).
  • Implemented a network slicing approach to allocate resources for FL training based on selected clients.
  • Compared the proposed FL deployment methodology against the federated averaging (FedAvg) algorithm.

Main Results:

  • The proposed RL-based FL deployment in O-RAN demonstrated superior performance compared to the federated averaging (FedAvg) algorithm.
  • Achieved faster convergence and required fewer communication rounds for model training.

Conclusions:

  • The developed methodology effectively deploys and optimizes federated learning tasks in O-RAN, enhancing distributed intelligence for 5G.
  • Reinforcement learning-based client selection and resource allocation are key to improving FL efficiency in O-RAN environments.