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Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
March 21, 2024, 4:43 a.m. | Joohyung Lee, Mohamed Seif, Jungchan Cho, H. Vincent Poor
cs.LG updates on arXiv.org arxiv.org
Abstract: Split Federated Learning (SFL) has recently emerged as a promising distributed learning technology, leveraging the strengths of both federated 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 …
abstract advantages arxiv attention concerns consumption convergence cs.ai cs.cr cs.lg distributed distributed learning energy federated learning industry innovation privacy technology type
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