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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

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.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the rated...
Energy Losses in Transformers01:21

Energy Losses in Transformers

In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the copper windings...
Types Of Transformers01:16

Types Of Transformers

Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
Differential Relays01:20

Differential Relays

Differential relays are used to protect generators, buses, and transformers by comparing electrical quantities at different points. When a fault occurs, the difference in current between the two points triggers the relay to operate, opening the circuit breaker. Under normal conditions, the current entering (i1) and leaving (i2) a generator are equal. When a fault occurs, however, these currents become unequal, and the difference current flows in the relay operating coil, causing the relay to...

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Related Experiment Videos

Fed-DiTTab: Diffusion transformer for tabular data generation in federated learning.

Yajun Pi1, Ming Zheng2, Fanhao Ma1

  • 1School of Computer and Information, Anhui Normal University, Wuhu, 241002, Anhui Province, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Federated learning struggles with imbalanced data. Fed-DiTTab uses DiTTab for oversampling minority classes, enhancing federated learning performance on imbalanced datasets while preserving privacy.

Keywords:
Classification tasksFederated learningImbalanced dataOversamplingPrivacy protection

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Imbalanced data is common in real-world classification, hindering traditional ML models.
  • Federated learning enables collaborative model training without data sharing, addressing privacy concerns.
  • Class imbalance significantly degrades federated learning performance.

Purpose of the Study:

  • To propose Fed-DiTTab, a novel method to address class imbalance in federated learning.
  • To enhance the performance of federated learning models on imbalanced datasets.
  • To maintain data privacy during collaborative training.

Main Methods:

  • Fed-DiTTab employs DiTTab for oversampling minority class samples on each client.
  • Clients collaboratively train a shared model using the oversampled data.
  • Experiments conducted on public datasets to validate the proposed method.

Main Results:

  • Fed-DiTTab significantly outperforms existing methods on imbalanced datasets.
  • The synthetic data mechanism is crucial for capturing minority class features.
  • The proposed method effectively mitigates class imbalance in federated learning.

Conclusions:

  • Fed-DiTTab offers an effective solution for class imbalance in federated learning.
  • The method preserves data privacy while improving model performance.
  • Oversampling minority classes is essential for handling imbalanced data in federated environments.