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Related Concept Videos

Maximum Size of Aggregate01:12

Maximum Size of Aggregate

157
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
157
Aggregates Classification01:29

Aggregates Classification

345
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
345
Associative Learning01:27

Associative Learning

441
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...
441
Unsoundness of Aggregate due to Volume Change01:26

Unsoundness of Aggregate due to Volume Change

132
Unsoundness in aggregates due to volume changes is primarily caused by the physical alterations aggregates undergo, such as freezing and thawing, thermal changes, and wetting and drying. Unsound aggregates, when subjected to these changes, result in volume change upon disintegration. This, in turn, contributes to the deterioration of concrete, including scaling, pop-outs, and cracking. Particular types of aggregates, such as porous flints, cherts, and those containing clay minerals, are...
132
Types of Aggregate Grading01:15

Types of Aggregate Grading

591
Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
591
Observational Learning01:12

Observational Learning

209
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...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Communication-Efficient and Privacy-Preserving Verifiable Aggregation for Federated Learning.

Kaixin Peng1, Xiaoying Shen2,3, Le Gao1

  • 1The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China.

Entropy (Basel, Switzerland)
|August 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a secure federated learning (FL) scheme protecting user privacy and aggregation integrity. It uses gradient encryption and hashing to prevent data inference and verify results, enhancing FL reliability.

Keywords:
federated learninghomomorphic hash functionprivacy protectionverifiability

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Area of Science:

  • Computer Science
  • Machine Learning
  • Cryptography

Background:

  • Federated learning (FL) enables decentralized model training without raw data sharing.
  • Untrusted servers in FL pose risks of private information inference and result manipulation.
  • Ensuring privacy and aggregation integrity is crucial for reliable FL deployment.

Purpose of the Study:

  • To propose a secure and verifiable federated learning scheme.
  • To enhance privacy protection against malicious servers.
  • To ensure the integrity of aggregated model results.

Main Methods:

  • Implemented a federated learning scheme with secure gradient aggregation.
  • Utilized single-mask technology for encrypting gradients to protect user data.
  • Employed hashing on masked gradients for verifying aggregation integrity.

Main Results:

  • The proposed scheme effectively prevents malicious servers from inferring private user information.
  • Verified the integrity of aggregation results through a hashing mechanism.
  • Demonstrated high communication efficiency suitable for bandwidth-constrained environments.

Conclusions:

  • The developed secure aggregation verifiable federated learning scheme offers robust privacy and integrity.
  • The protocol is particularly effective for scenarios with limited bandwidth and offline users.
  • This approach advances the security and trustworthiness of federated learning systems.