Add to ORKGThe aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contribution of a given data point from a training procedure. Federated Unlearning (FU) consists in extending MU to unlearn a given client's contribution from a federated training routine. Current FU approaches are generally not scalable, and do not come with sound theoretical quantification of the effectiveness of unlearning. In this work we present Informed Federated Unlearning (IFU), a novel efficient and quantifiable FU approach. Upon unlearning request from a given client, IFU identifies the optimal FL iteration from which FL has to be reinitialized, with unlearning guarantees obtained through a randomized perturbation mechanism. The theory of...
Federated Learning (FL) is a well-established technique for privacy preserving distributed training....
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framew...
Federated learning (FL) is a distributed model training paradigm that preserves clients' data privac...
The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contri...
The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contri...
With privacy legislation empowering users with the right to be forgotten, it has become essential to...
In Machine Learning, the emergence of \textit{the right to be forgotten} gave birth to a paradigm na...
To protect users' right to be forgotten in federated learning, federated unlearning aims at eliminat...
Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powe...
Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training ...
Federated Learning (FL) offers a collaborative training framework, allowing multiple clients to cont...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Unlearning the data observed during the training of a machine learning (ML) model is an important ta...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
We propose a solution to address the lack of high-probability guarantees in Federated Learning (FL) ...
Federated Learning (FL) is a well-established technique for privacy preserving distributed training....
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framew...
Federated learning (FL) is a distributed model training paradigm that preserves clients' data privac...
The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contri...
The aim of Machine Unlearning (MU) is to provide theoretical guarantees on the removal of the contri...
With privacy legislation empowering users with the right to be forgotten, it has become essential to...
In Machine Learning, the emergence of \textit{the right to be forgotten} gave birth to a paradigm na...
To protect users' right to be forgotten in federated learning, federated unlearning aims at eliminat...
Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powe...
Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training ...
Federated Learning (FL) offers a collaborative training framework, allowing multiple clients to cont...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
Unlearning the data observed during the training of a machine learning (ML) model is an important ta...
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this...
We propose a solution to address the lack of high-probability guarantees in Federated Learning (FL) ...
Federated Learning (FL) is a well-established technique for privacy preserving distributed training....
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framew...
Federated learning (FL) is a distributed model training paradigm that preserves clients' data privac...