With the rapid growth of the Internet of Things (IoT) and edge intelligence, federated learning (FL) has become a promising approach for distributed model training while preserving privacy. However, the resource constraints of IoT devices, including limited communication bandwidth and heterogeneous computational and energy capabilities, hinder the scalability and performance of FL. To address these challenges, we propose the Air-FedSNN framework, which integrates slimmable neural networks (SNNs) with over-the-air computation (AirComp) to improve communication, computation, and energy efficiency in heterogeneous IoT networks. A width-aware aggregation mechanism is developed to mitigate the impact of varying model widths and aggregation errors. Additionally, a dynamic width allocation strategy based on Lyapunov optimization and multi-armed bandit (MAB) theory is introduced to adaptively select model widths under long-term energy constraints, balancing energy efficiency and training performance without prior knowledge of device capabilities. Extensive experiments on MNIST, CIFAR-10 and CIFAR-100 datasets demonstrate that Air-FedSNN outperforms conventional FL methods, achieving superior accuracy-energy tradeoffs, and provides an efficient solution for resource-constrained environments.
Yue Zhang, Guopeng Zhang, Ke-Zhi Wang et al.· IEEE Journal on Selected Are...· 0 citations
Mobile network operators are increasingly exploring the use of artificial intelligence (AI) to automate complex network tasks, such as cell selection and mobility management. A fundamental problem arises: there is currently no way to verify that an AI function is making the right decisions or for the right reasons, rather than arriving at correct-looking answers through unreliable shortcuts. In safety-critical and resilience-focused infrastructure, this lack of transparency poses a significant challenge to the widespread adoption of AI technologies in wireless networks. In this paper, we propose a mechanical auditing approach: inspecting a function's internal representations and checking them against machine-verifiable 3GPP specifications. Specifically, we set out a general three-step auditing principle that locates protocol-relevant features, verifies their causal role, and diagnoses how adaptation reshapes their use, grounding it throughout publicly available interpretability and telecommunications research. We present an audit-native network architecture in which a dedicated verification agent continuously checks the reasoning of AI functions in networks, supporting both predeployment certification and runtime auditing. We also discuss how it could be realised, the data and benchmarks, as well as the open challenges that remain before mechanistic auditing can enter telecommunications practice and standardisation.
