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Related Concept Videos

Forgetting01:21

Forgetting

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Forgetting is an intrinsic aspect of human memory, characterized by the gradual loss or inaccessibility of information over time. Hermann Ebbinghaus, a pioneering psychologist, extensively studied this phenomenon and formulated the forgetting curve. This curve illustrates that memory loss occurs rapidly immediately after learning and then decelerates over time. Several mechanisms contribute to forgetting, including encoding failure, storage decay, retrieval failure, and interference.
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Related Experiment Videos

Forgettable Federated Linear Learning With Certified Data Unlearning.

Ruinan Jin, Minghui Chen, Qiong Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |April 22, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new federated learning (FL) method for efficient and secure federated unlearning (FU) in deep neural networks. The novel approach, Forgettable Federated Linear Learning (F²L²), enables model unlearning without retraining or client communication.

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Cybersecurity

    Background:

    • Federated learning (FL) enables collaborative model training while preserving user privacy.
    • Federated unlearning (FU) addresses data privacy and security by removing specific data influences without full retraining.
    • Current FU methods face challenges like communication overhead, security risks, and memory constraints, especially for deep neural networks (DNNs).

    Purpose of the Study:

    • To develop a novel, efficient, and secure federated unlearning strategy for deep neural networks.
    • To address the limitations of existing FU methods, including communication overhead, storage requirements, and lack of theoretical certification.
    • To enable the removal of specific client data's impact from a trained model without compromising overall model performance or requiring retraining.

    Main Methods:

    • Introduced Forgettable Federated Linear Learning (F²L²), a strategy that approximates DNNs linearly using pretrained models for federated linear training (FLT).
    • Developed FedRemoval, a certified, efficient, and secure unlearning algorithm for the server to unlearn target clients.
    • FedRemoval operates without requiring client communication or additional model storage, enhancing efficiency and security.

    Main Results:

    • F²L² achieves performance comparable to original networks through federated linear training.
    • FedRemoval successfully unlearns target clients efficiently and securely, validated across various datasets and model architectures.
    • Extensive experiments on convolutional neural networks and foundation models demonstrate F²L²'s effectiveness in balancing model accuracy and unlearning.
    • The proposed method shows promise for efficient and trustworthy federated unlearning.

    Conclusions:

    • F²L² provides a novel pipeline for efficient and trustworthy federated unlearning in deep neural networks.
    • The FedRemoval strategy overcomes key limitations of existing FU methods, offering a certified and practical solution.
    • This work advances the field of federated learning by enabling robust and privacy-preserving data removal mechanisms.