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How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?
To alleviate the training burden in federated learning while enhancing convergence speed, Split Federated Learning (SFL) has emerged as a promising approach by combining the advantages of federated and split learning. However, despite its advantages, existing SFL studies have largely overlooked the
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