## 1 Introduction
Furthermore, these techniques may degrade model accuracy due to the added computational complexity and data perturbation, which is particularly problematic for high-precision tasks like anomaly detection or medical monitoring in IoT-based healthcare [19]. To this end, this paper proposes a new framework that integrates hierarchical federated learning, asynchronous aggregation, and lightweight encryption to address key challenges effectively. The framework employs a hierarchical architecture based on an IoT-Edge-Cloud model, where data processing and aggregation occur at multiple levels to enhance both efficiency and scalability. In particular, the hierarchical structure facilitates local aggregation at the edge, which reduces the communication overhead on the central server and improves overall scalability. Moreover, asynchronous aggregation alleviates the issue of stragglers by allowing devices to submit updates independently without the need for synchronization with all other nodes. Additionally, we employ the lightweight Salsa20 encryption to protect data during transmission [20], effectively countering man-in-the-middle attacks while maintaining low computational overhead suitable for constrained devices. Additionally, we employ the lightweight Salsa20 encryption to secure data transmission, protecting a range of potential threats, including eavesdropping and data tampering, while maintaininglow computational overhead suitable for resource-constrained devices [20]. The detailed security analysis, including the algorithm's resilience to common attack vectors, is presented in Section 3.5.4. Unlike traditional FL and existing hierarchical frameworks, the proposed framework uniquely integrates asynchronous aggregation and lightweight encryption into a hierarchical design. This integration not only addresses straggler issues by decoupling global synchronization but also ensures secure data transmission without imposing prohibitive computational overhead. In doing so, our approach effectively balances scalability, privacy, and efficiency, making it particularly suitable for IoT environments characterized by dynamic heterogeneity and limited resources. The key contributions of this paper are as follows:  
* **Hierarchical Federated-Learning Framework:** We design an HFL framework tailored for heterogeneous IoT environments, leveraging edge-level aggregation to reduce communication overhead and enhance privacy. * **Asynchronous Aggregation:** An asynchronous aggregation strategy is introduced to mitigate straggler effects, enabling efficient training by decoupling slow devices from global synchronization. * **Lightweight Encryption:** Salsa20 is employed as a lightweight encryption mechanism, ensuring secure data transmission with minimal computational overheads and addressing privacy concerns in resource-constrained IoT devices. * **Experimental Validation:** We validate the framework through extensive experiments in a real-world IoT scenario, demonstrating a 20% reduction in round time compared to traditional FL methods without compromising model accuracy. The proposed framework is demonstrated through a human activity recognition (HAR) use case, showcasing its applicability in privacy-sensitive and accuracy-critical scenarios. Our results highlight the effectiveness of integrating hierarchical design, asynchronous updates, and lightweight encryption to overcome the scalability, efficiency, and privacy challenges in heterogeneous IoT environments. **Relationship with Existing FL Research:** The proposed framework is designed to address the unique challenges of system heterogeneity, scalability, and privacy in IoT environments. Unlike existing techniques such as personalization [21], pruning [22], and masking [23], which focus on optimizing specific aspects of federated learning, Our framework tackles systemic variations and potential stragglers in hierarchical IoT networks. These approaches are complementary to our framework and could be integrated into the hierarchical framework to further enhance model performance.