How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?

T1Covered by 1 source · first reported by arXiv at 24 Sept, 04:00 UTC

arXiv:2412.07813v5 Announce Type: replace-cross Abstract: 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…

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• T1How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?arXiv24 Sept, 04:00