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An adaptive split federated learning framework for distributed AI training in 6G computing-power network
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DOI：10.23919/jcc.fa.2025-0275.202604.png)
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Abstract
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To tackle the formidable challenges from future AI applications in communication and computing power, an urgent need arises for a scalable distributed learning framework, which should adeptly orchestrate the varied resources of widely dispersed nodes within 6G networks, offering flexible connectivity and computing services. For distributed AI training in 6G computing-power network (CPN), this paper proposes an adaptive split federated learning (SFL) framework. Considering the terminal computing power heterogeneity, it introduces three train modes: local-only, single base station (BS) collaboration, and dual BSs collaboration. Given the varying channel qualities, two transmission modes are used in the model aggregation phase: direct uploading to the BS and uploading via Device-to-Device (D2D) relays. Accordingly, we formulate a joint optimization problem to minimize overall task latency, which involves model splitting method, cooperative node selection, and multi-domain resource allocation, and then decompose it into two subproblems. First, a shortest path search algorithm is devised to solve the optimal model splitting method and cooperative node selection. Second, convex optimization is employed to derive the optimal multi-domain resource allocation. Simulation results show that the proposed framework attains lower total training latency while preserving high model accuracy.
Keywords:
computing-power network
federated learning
6G
resource allocation
split learning
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