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Related Concept Videos

Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

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Related Experiment Videos

FuGuard: Client-Level Federated Unlearning via Generative Surrogates and Optimal Transport.

Pian Qi, Daniela Annunziata, Chiara Jappelli

    IEEE Transactions on Neural Networks and Learning Systems
    |July 13, 2026
    PubMed
    Summary

    Federated unlearning efficiently removes client data influence from models. FuGuard achieves ideal data removal while maintaining global model performance, outperforming existing methods.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Data Privacy
    • Cybersecurity

    Background:

    • Federated learning (FL) enables collaborative training but faces challenges with data privacy and removal requests.
    • Existing federated unlearning methods are often computationally expensive or ineffective.

    Purpose of the Study:

    • To develop an efficient federated unlearning framework for client-level data removal.
    • To ensure the removal of specific data's influence without compromising global model performance.

    Main Methods:

    • Proposing FuGuard, a dual-strategy framework combining generative surrogates and optimal transport regularization.
    • Approximating client contribution and constraining parameter drift for effective unlearning.

    Main Results:

    • FuGuard demonstrated significant reduction in target client data influence.
    • The framework maintained global model performance and outperformed state-of-the-art baselines.
    • Evaluated using backdoor and member inference attacks to confirm forgetting effectiveness.

    Conclusions:

    • FuGuard offers an efficient and effective solution for federated unlearning.
    • The proposed method balances data removal with model integrity in federated systems.