Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
IEEE Network(2023)
摘要
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 can have a
substantial impact on the energy consumption of clients and their privacy, as
it influences the training burden and the output of the client-side models.
Moreover, the design challenge of determining the cut layer is highly
intricate, primarily due to the inherent heterogeneity in the computing and
networking capabilities of clients. In this article, we provide a comprehensive
overview of the SFL process and conduct a thorough analysis of energy
consumption and privacy. This analysis takes into account the influence of
various system parameters on the cut layer selection strategy. Additionally, we
provide an illustrative example of the cut layer selection, aiming to minimize
the risk of clients from reconstructing the raw data at the server while
sustaining energy consumption within the required energy budget, which involve
trade-offs. Finally, we address open challenges in this field. These directions
represent promising avenues for future research and development.
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