Federated Split Learning With Non-IID Data: Convergence Analysis and Resource Allocation
Huiqing Ao, Hui Tian, Wanli Ni, Dusit Tao Niyato · IEEE Transactions on Cognitive Communications and Networking · 2026
The growing architectural complexity of artificial intelligence models poses significant obstacles to the realization of pervasive intelligence on resource-constrained Internet of Things devices. Federated split learning (FSL) has emerged as a promising solution that enhances training efficiency by combining the benefits of model splitting and parallel computing. However, existing FSL encounters degraded model performance and training inefficiency due to data and device heterogeneity in wireless networks. To address these issues, we propose a gradient-compensation FSL (GCFSL) scheme that leverages complementary gradient information across devices. We derive the convergence upper bound by considering the impact of data heterogeneity and split layer selection of deep neural networks. To minimize the long-term training latency while guaranteeing learning performance in resource-constrained wireless networks, we formulate a two-timescale optimization problem. By decomposing the problem into short-term and long-term subproblems, we design iterative optimization algorithms to solve them with low computational complexities. Numerical results demonstrate that our GCFSL scheme achieves superior model accuracy with low communication overhead while attaining fast convergence under both independent and identically distributed (IID) and non-IID settings, outperforming state-of-the-art benchmarks. Moreover, the proposed algorithm can achieve a low training latency compared with its non-optimized counterpart.