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   Articles with "federated unlearning" as a keyword



A Survey of Challenges and Opportunities in Vertical Federated Unlearning

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recommendations! Published in 2025 at "IEEE Access"

DOI: 10.1109/access.2025.3600884

Abstract: Vertical Federated Unlearning (VFU) is an emerging research area focused on removing specific data contributions from models trained under Vertical Federated Learning (VFL), a setting where different organizations collaboratively train models using distinct feature sets… read more here.

Keywords: challenges opportunities; survey challenges; vfu; federated unlearning ... See more keywords

Federated Unlearning: Guarantee the Right of Clients to Forget

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recommendations! Published in 2022 at "IEEE Network"

DOI: 10.1109/mnet.001.2200198

Abstract: The Right to be Forgotten gives a data owner the right to revoke their data from an entity storing it. In the context of federated learning, the Right to be Forgotten requires that, in addition… read more here.

Keywords: federated unlearning; unlearning guarantee; clients forget; right clients ... See more keywords

Guaranteeing Data Privacy in Federated Unlearning With Dynamic User Participation

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recommendations! Published in 2024 at "IEEE Transactions on Dependable and Secure Computing"

DOI: 10.1109/tdsc.2024.3476533

Abstract: Federated Unlearning (FU) is gaining prominence for its capability to eliminate influences of specific users’ data from trained global Federated Learning (FL) models. A straightforward FU method involves removing the unlearned user-specified data and subsequently… read more here.

Keywords: unlearning efficiency; privacy; federated unlearning; user participation ... See more keywords

FedWiper: Federated Unlearning via Universal Adapter

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recommendations! Published in 2025 at "IEEE Transactions on Information Forensics and Security"

DOI: 10.1109/tifs.2025.3557671

Abstract: Privacy preservation are becoming increasingly significant in machine learning, with recent privacy regulations requiring the deletion of personal data and its impact on models. Although erasing data from storage is simple, removing the influence of… read more here.

Keywords: fedwiper federated; federated unlearning; exact unlearning; model ... See more keywords

F2UL: Fairness-Aware Federated Unlearning for Data Trading

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recommendations! Published in 2024 at "IEEE Transactions on Mobile Computing"

DOI: 10.1109/tmc.2024.3429228

Abstract: Federated learning (FL) offers a credible solution for distributed data trading since it could train machine learning models in a distributed manner thereby enhancing data privacy without sharing local data. However, it is still challenging… read more here.

Keywords: quality; federated unlearning; data trading; model ... See more keywords