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Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives

Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives

Jiaojiao Zhang
Dominik Fay
M. Johansson
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Papers citing "Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives"

5 / 5 papers shown
Title
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
Ori Peleg
Natalie Lang
Stefano Rini
Nir Shlezinger
Kobi Cohen
FedML
48
0
0
17 Mar 2025
A survey on secure decentralized optimization and learning
A survey on secure decentralized optimization and learning
Changxin Liu
Nicola Bastianello
Wei Huo
Yang Shi
Karl H. Johansson
26
1
0
16 Aug 2024
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy
Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy
Wei Huo
Changxin Liu
Kemi Ding
Karl H. Johansson
Ling Shi
FedML
30
0
0
08 Aug 2024
Personalized Privacy Amplification via Importance Sampling
Personalized Privacy Amplification via Importance Sampling
Dominik Fay
Sebastian Mair
Jens Sjölund
39
0
0
05 Jul 2023
Local Graph-homomorphic Processing for Privatized Distributed Systems
Local Graph-homomorphic Processing for Privatized Distributed Systems
Elsa Rizk
Stefan Vlaski
A. H. Sayed
FedML
13
1
0
26 Oct 2022