A closed-form Nash equilibrium and Stackelberg search show how an SFL owner should set incentives and cut layer to elicit client data contributions.
Accelerating Federated Learning with Split Learning on Locally Generated Losses,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.GT 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?
A closed-form Nash equilibrium and Stackelberg search show how an SFL owner should set incentives and cut layer to elicit client data contributions.