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The Cloud-Edge-IoT (CEI) continuum integrates edge computing, cloud computing, and the Internet of Things (IoT), fostering rapid Industrial Internet of Things (IIoT) development. Despite its potential, it faces significant challenges, including robustness issues, communication-induced latency, and inconsistent model convergence due to system and data heterogeneity. Machine Learning (ML), a vital technology in this domain, further complicates privacy and overhead concerns. To mitigate these issues, Federated Learning (FL) appeared as a promising solution where the FL setting allows the devices to collaboratively train a model while keeping training data local. However, in practice, it suffers from several issues such as robustness (due to a single point of failure), latency (it still requires a significant amount of communication resources), and model convergence (due to the heterogeneity of system and statistics). To cope with these issues, we propose to integrate Hierarchical FL (HFL) and Spiking Neural Networks (SNN) into the framework for building a scalable and energy-efficient solution for the industrial CEI continuum. We present an in-depth overview, discussions on emerging applications, and a performance evaluation via a case study in IoT image classification. We also identify and explore open research topics crucial for the future realization of such a continuum.