C-SFL splits the model into three parts across weak clients, local aggregators, and a server, enabling per-epoch aggregation of the middle portion, and reports reduced delay and communication with improved accuracy in experiments.
IEEE Transac- tions on Neural Networks and Learning Systems34(12), 10374–10386 (2022) 14 Yiannis Papageorgiou et al
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Collaborative Split Federated Learning with Parallel Training and Aggregation
C-SFL splits the model into three parts across weak clients, local aggregators, and a server, enabling per-epoch aggregation of the middle portion, and reports reduced delay and communication with improved accuracy in experiments.