Nowadays, split federated learning (SFL) has emerged as an effective paradigm for enabling privacy-preserving collaborative intelligence across heterogeneous devices with limited computation. However, SFL incurs significant communication overhead in wireless networks due to the uplink transmission of high-dimensional smashed data, which degrades network efficiency. To mitigate the communication bottleneck, we propose a prototype-based SFL framework ProtoSFL. Specifically, each selected client computes local prototypes for observed classes and uploads them to the server. Based on the received prototypes, the server derives global prototypes and optimizes a weighted objective that combines classification loss with prototype alignment loss. The server then updates the model accordingly and returns personalized prototype gradients to the clients. Simulation results verify the effectiveness of ProtoSFL in reducing communication overhead, achieving a substantial reduction in uplink communication, while maintaining competitive testing accuracy under various heterogeneous data settings compared with SFL baselines.
Results support the central conclusion that lightweight joint scheduling can materially improve wall-clock FL efficiency in heterogeneous 5G/6G edge networks.
Split Federated Learning (SFL) has emerged as a pivotal paradigm for privacy-preserving distributed training on resource-constrained edge devices by partitioning neural networks between clients and a server. A critical design choice in SFL is the split layer, which determines the computation distribution and the semant...
Ai-Jing Li, Ya-Wen Li, Guan-Hua Ye et al.· Proceedings of the Thirty-Fi...· 0 citations
With the emergence of next-generation communication technologies, the integration of AI into next-generation wireless networks is becoming an important step toward achieving distributed intelligence. Different distributed learning frameworks, such as federated learning (FL) and split learning (SL), enable collaborative...
Zhe Wang, Sige Liu, Nikolaos G. Bartzoudis et al.· IEEE Communications Letters· 0 citations
Integrating over-the-air computations into the model aggregation process of federated learning (FL) offers a promising solution to mitigate the communication bottleneck in FL model training. In this approach, all the clients modulate their intermediate parameters, such as gradients, onto the same set of orthogonal wave...
Jiaqi Zhu, H. Yang, Nikolaos Pappas et al.· IEEE Transactions on Wireles...· 0 citations
Experimental results on heterogeneous MNIST and CIFAR-10 settings show that QEF-GT-AdamW consistently improves robustness and convergence performance over representative DecL baselines while achieving favorable accuracy-communication trade-offs under limited wireless resources.
Thieu Van Nguyen, T. Nguyen, Ons Aouedi et al.· 0 citations
Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving distributed machine learning across mobile and edge devices. Nevertheless, the presence of non-independent and identically distributed (non-IID) data in wireless networks often leads to severe gradient divergence, slow convergence, and d...
