208 citations · 448 across the 32 of their papers we have counts for
4 papers · 1 filter
Robust Learning Protocol for Federated Tumor Segmentation Challenge
Ambrish Rawat, Giulio Zizzo, Swanand Kadhe +2
In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022). Enab…
Federated Unlearning: How to Efficiently Erase a Client in FL?
Anisa Halimi, Swanand Kadhe, Ambrish Rawat +1
With privacy legislation empowering the users with the right to be forgotten, it has become essential to make a model amenable for forgetting some of its training data. However, ex…
Challenges and Pitfalls of Bayesian Unlearning
Ambrish Rawat, James Requeima, Wessel Bruinsma +1
Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model. Approximate unlearning are one class of methods…
Towards an Accountable and Reproducible Federated Learning: A FactSheets Approach
Nathalie Baracaldo, Ali Anwar, Mark Purcell +8
Federated Learning (FL) is a novel paradigm for the shared training of models based on decentralized and private data. With respect to ethical guidelines, FL is promising regarding…