Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks

CA Arevalo, SL Noorbakhsh, Y Dong, Y Hong… - Proceedings of the …, 2024 - ojs.aaai.org
Federated learning (FL) has been widely studied recently due to its property to
collaboratively train data from different devices without sharing the raw data. Nevertheless …
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Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning Against Attribute Inference Attacks

C Arroyo Arevalo, SL Noorbakhsh, Y Dong… - arXiv e …, 2023 - ui.adsabs.harvard.edu
Federated learning (FL) has been widely studied recently due to its property to
collaboratively train data from different devices without sharing the raw data. Nevertheless …

Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks

C Arroyo Arevalo, SL Noorbakhsh, Y Dong… - Proceedings of the …, 2024 - par.nsf.gov
Federated learning (FL) has been widely studied recently due to its property to
collaboratively train data from different devices without sharing the raw data. Nevertheless …

Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning Against Attribute Inference Attacks

CA Arevalo, SL Noorbakhsh, Y Dong, Y Hong… - arXiv preprint arXiv …, 2023 - arxiv.org
Federated learning (FL) has been widely studied recently due to its property to
collaboratively train data from different devices without sharing the raw data. Nevertheless …