Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning. (arXiv:2311.09441v2 [cs.LG] UPDATED)

Split Federated Learning (SFL) has recently emerged as a promising
distributed learning technology, leveraging the strengths of both federated
learning and split learning. It emphasizes the advantages of rapid convergence
while addressing privacy concerns. As a result, this innovation has received
significant attention from both industry and academia. However, since the model
is split at a specific layer, known as a cut layer, into both client-side and
server-side models for the SFL, the choice of the cut layer in SFL …

academia attention convergence distributed energy federated federated learning industry innovation privacy privacy concerns rapid result split learning technology