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

As an edge intelligence algorithm for multi-device collaborative training, federated learning (FL) can protect data privacy but increase the computing load of wireless devices. In contrast, split learning (SL) can reduce the computing load of devices by model splitting and assignment. To take advantage of FL and SL, we propose a hybrid federated split learning (HFSL) framework for wireless networks in this paper, which combines the multi-worker collaborative training of FL and the flexible splitting of SL. To reduce the computational idleness in model splitting, we design a parallel computing scheme for model splitting without label sharing and conduct a theoretical analysis of the impact of the delayed gradient on the convergence. Aiming to obtain the trade-off between the training time and energy consumption, we model the joint optimization problem of splitting decisions, the bandwidth, and computing resources as a multi-objective problem. As such, we propose a predictive generative adversarial network (GAN)-powered multi-objective optimization algorithm to obtain the Pareto front of the problem, which utilizes the discriminator to guide the training of the generator to predict promising solutions. Experimental results demonstrate that the proposed algorithm outperforms the considered baselines in finding Pareto optimal solutions, and the solutions obtained from the proposed HFSL framework can dominate the solution of FL.