Communication efficient and provable federated unlearning
We study federated unlearning, a novel problem to eliminate the impact of specific clients or
data points on the global model learned via federated learning (FL). This problem is driven …
data points on the global model learned via federated learning (FL). This problem is driven …
[PDF][PDF] Communication Efficient and Provable Federated Unlearning
Y Tao, CL Wang, M Pan, D Yu, X Cheng, D Wang - vldb.org
We study federated unlearning, a novel problem to eliminate the impact of specific clients or
data points on the global model learned via federated learning (FL). This problem is driven …
data points on the global model learned via federated learning (FL). This problem is driven …
Communication Efficient and Provable Federated Unlearning
Y Tao, CL Wang, M Pan, D Yu, X Cheng, D Wang - 2024 - repository.kaust.edu.sa
We study federated unlearning, a novel problem to eliminate the impact of specific clients or
data points on the global model learned via federated learning (FL). This problem is driven …
data points on the global model learned via federated learning (FL). This problem is driven …
[引用][C] Communication Efficient and Provable Federated Unlearning
Y Tao, CL Wang, M Pan, D Yu, X Cheng… - Proc. VLDB Endow …, 2024 - openreview.net
Communication Efficient and Provable Federated Unlearning | OpenReview OpenReview.net
Login Open Peer Review. Open Publishing. Open Access. Open Discussion. Open …
Login Open Peer Review. Open Publishing. Open Access. Open Discussion. Open …
Communication Efficient and Provable Federated Unlearning
Y Tao, CL Wang, M Pan, D Yu, X Cheng… - Proceedings of the VLDB …, 2024 - dl.acm.org
We study federated unlearning, a novel problem to eliminate the impact of specific clients or
data points on the global model learned via federated learning (FL). This problem is driven …
data points on the global model learned via federated learning (FL). This problem is driven …
Communication Efficient and Provable Federated Unlearning
Y Tao, CL Wang, M Pan, D Yu, X Cheng… - arXiv e …, 2024 - ui.adsabs.harvard.edu
We study federated unlearning, a novel problem to eliminate the impact of specific clients or
data points on the global model learned via federated learning (FL). This problem is driven …
data points on the global model learned via federated learning (FL). This problem is driven …