The paper derives the first convergence upper bound for split federated learning under activation upload, gradient download, and aggregation failures, then jointly optimizes client sampling and model splitting to minimize the bound, with simulations on EMNIST and CIFAR-10.
Pairingfl: Efficient federated learning with model splitting and client pairing
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Optimizing Split Federated Learning with Unstable Client Participation
The paper derives the first convergence upper bound for split federated learning under activation upload, gradient download, and aggregation failures, then jointly optimizes client sampling and model splitting to minimize the bound, with simulations on EMNIST and CIFAR-10.