Client Selection in Federated Learning for Industry 5.0: A Heuristic-Guided Pointer Network Reinforcement Learning Approach

Federated learning (FL) offers a promising distributed paradigm for managing massive data from Industry 5.0 devices while preserving privacy. However, significant challenges arise from inherent system heterogeneity and data heterogeneity across devices. These factors severely impede FL performance, leading to slow convergence and potential degradation of the global model’s accuracy. Random client selection strategies are often insufficient to mitigate these issues effectively. To address these limitations, we propose FedHRL: a heuristic-guided pointer network reinforcement learning framework for joint client selection and bandwidth allocation in FL. Specifically, our heuristic-guided soft actor–critic algorithm employs a transformer-based pointer network within its action network to tackle the combinatorial optimization problem of sequentially selecting clients and allocating bandwidth. This network identifies the optimal next client based on prior selections and available bandwidth constraints. Furthermore, to accelerate RL convergence and enhance policy effectiveness, we integrate a particle swarm optimization-based bandwidth reallocation strategy, which refines the RL agent’s bandwidth allocation decisions, feeding the optimization results back as an enhanced reward signal to expedite learning and improve overall performance. Experiments demonstrate that FedHRL accelerates FL training convergence while maintaining high model accuracy in heterogeneous environments.