The challenges of scalability, robustness, and resilience to slow devices have posed significant obstacles to the effective and efficient implementation of Federated Learning (FL), a crucial technology for the emerging Artificial Intelligence of Things (AIoT). This paper proposes a solution to these challenges with the introduction of a Decentralized Hierarchical Asynchronous Federated Learning Scheme (DHA-FL). This scheme utilizes a hierarchical edge computing architecture, enabling a two-stage model aggregation paradigm that significantly enhances system scalability. To further enhance system robustness, decentralized asynchronous model aggregation is adopted among edge servers to prevent single node failures while mitigating the impact of slow devices or stragglers. Our experiments, conducted on a live wireless multi-hop IoT testbed, demonstrate that DHA-FL can achieve convergence in approximately half the time compared to the centralized hierarchical approach. Moreover, it enables an even more significant convergence speed-up (up to 8x) over the classic FedAvg baseline when dealing with stragglers.