Search Results for author: Reihaneh Torkzadehmahani

Found 9 papers, 5 papers with code

Improved Localized Machine Unlearning Through the Lens of Memorization

no code implementations3 Dec 2024 Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis, Daniel Rueckert, Gintare Karolina Dziugaite, Eleni Triantafillou

We also propose a new unlearning algorithm, Deletion by Example Localization (DEL), that resets the parameters deemed-to-be most critical according to our localization strategy, and then finetunes them.

Machine Unlearning Memorization

Label Noise-Robust Learning using a Confidence-Based Sieving Strategy

no code implementations11 Oct 2022 Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert, Georgios Kaissis

Identifying the samples with noisy labels and preventing the model from learning them is a promising approach to address this challenge.

Kernel Normalized Convolutional Networks

1 code implementation20 May 2022 Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert, Georgios Kaissis

Existing convolutional neural network architectures frequently rely upon batch normalization (BatchNorm) to effectively train the model.

Federated Learning Image Classification +1

Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments

no code implementations13 Nov 2020 Reza Nasirigerdeh, Mohammad Bakhtiari, Reihaneh Torkzadehmahani, Amirhossein Bayat, Markus List, David B. Blumenthal, Jan Baumbach

Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) across the clients.

Federated Learning

DP-CGAN: Differentially Private Synthetic Data and Label Generation

1 code implementation27 Jan 2020 Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten

Generative Adversarial Networks (GANs) are one of the well-known models to generate synthetic data including images, especially for research communities that cannot use original sensitive datasets because they are not publicly accessible.

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