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As an edge intelligence algorithm for multi-device collaborative training,federated learning (FL) can reduce the communication burden but increase thecomputing load of wireless devices. In contrast, split learning (SL) can reducethe computing load of devices by using model splitting and assignment, butincrease the communication burden to transmit intermediate results. In thispaper, to exploit the advantages of FL and SL, we propose a hybrid federatedsplit learning (HFSL) framework in wireless networks, which combines themulti-worker parallel update of FL and flexible splitting of SL. To reduce thecomputational idleness in model splitting, we design a parallel computingscheme for model splitting without label sharing, and theoretically analyze theinfluence of the delayed gradient caused by the scheme on the convergencespeed. Aiming to obtain the trade-off between the training time and energyconsumption, we optimize the splitting decision, the bandwidth and computingresource allocation. The optimization problem is multi-objective, and we thuspropose a predictive generative adversarial network (GAN)-poweredmulti-objective optimization algorithm to obtain the Pareto front of theproblem. Experimental results show that the proposed algorithm outperformsothers in finding Pareto optimal solutions, and the solutions of the proposedHFSL dominate the solution of FL.

Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning