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FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization
In federated learning, particularly in cross-device scenarios, secure aggregation has recently gained popularity as it effectively defends against inference attacks by malicious aggregators.
Machine unlearning through fine-grained model parameters perturbation
Machine unlearning techniques, which involve retracting data records and reducing influence of said data on trained models, help with the user privacy protection objective but incur significant computational costs.
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning
New categories may be introduced over time, or existing categories may need to be reclassified.
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