Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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...
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...
Masking and Demasking Agents01:19

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on the metal...
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...
Reinforcement01:23

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A three-tier stackelberg game-based hierarchical optimization framework for integrated electric vehicle battery swapping and charging systems.

Scientific reports·2026
Same author

Functional properties of corn starch films incorporated with carbon quantum dots for potential active packaging applications.

International journal of biological macromolecules·2026
Same author

Brain tumor segmentation using dual-stream multiscale 3D-UNET with dense net and spatial attention.

Scientific reports·2026
Same author

Reconfigurable intelligent surface and UAV coordination for reliable THz wireless networks.

PloS one·2026
Same author

AI based decision-making system for tooling design of aircraft product assembly with developed knowledge retrieval algorithm.

Scientific reports·2026
Same author

A hybrid federated learning framework with generative AI for privacy-preserving and sustainable security in IOT-enabled smart environments.

Scientific reports·2026

Related Experiment Videos

Federated deep reinforcement learning for privacy-preserving offloading in vehicular edge computing.

Alaa M Momani1, Deema Mohammed Alsekait2, Mahmoud Ahmad Al-Khasawneh1

  • 1School of Computing, Horizon University College, Ajman, UAE.

Scientific Reports
|June 10, 2026
PubMed
Summary

This study introduces a privacy-aware federated deep reinforcement learning (FDRL) framework for vehicular edge computing task offloading. The FDRL framework enhances system performance and reduces communication overhead while protecting vehicle data privacy.

Keywords:
Deep reinforcement learningFederated learningPrivacy preservationSoft actor–criticTask offloadingVehicular edge computing

Related Experiment Videos

Area of Science:

  • Vehicular communication networks
  • Edge computing
  • Machine learning for intelligent transportation systems

Background:

  • Internet of Vehicles (IoV) applications demand high computation and low latency.
  • Limited vehicular computing power necessitates edge offloading.
  • Centralized learning for offloading increases communication overhead and data exposure.

Purpose of the Study:

  • To propose a privacy-aware federated deep reinforcement learning (FDRL) framework for vehicular edge computing task offloading.
  • To address challenges of limited vehicle computing power, latency requirements, and data privacy in dynamic mobility environments.
  • To improve system cost, delay, energy efficiency, and deadline reliability through intelligent offloading decisions.

Main Methods:

  • Developed a novel FDRL framework integrating federated learning and deep reinforcement learning (DRL).
  • Incorporated four key mechanisms: hybrid action representation, personalized federated aggregation, task-criticality-aware deadline reliability, and a handover-aware multi-RSU model.
  • Trained local Soft Actor-Critic (SAC) policies using model parameters shared with the federated coordinator, not raw data.

Main Results:

  • The proposed FDRL framework demonstrated competitive or superior performance in system cost, delay, energy consumption, and deadline violation compared to baseline methods.
  • Achieved reduced communication overhead through federated learning, sharing model parameters instead of raw vehicular data.
  • The framework effectively handles non-IID data and dynamic handover scenarios in multi-RSU environments.

Conclusions:

  • The FDRL framework offers an effective solution for privacy-aware task offloading in vehicular edge computing.
  • It balances computational demands, latency constraints, and data privacy concerns in IoV environments.
  • Future work could explore formal privacy guarantees against inference attacks on model updates.