AI-Driven Lightweight and Trustworthy Federated Learning Framework for Decision-Making in Resource-Constrained Edge-IoT

Artificial Intelligence (AI) and the Internet of Things (IoT) are transforming modern computing by enabling intelligent decision-making across distributed devices. Federated Learning (FL) facilitates decentralized model training while preserving data privacy, making it ideal for large-scale AI applications. However, FL faces a major challenge of high communication overhead due to frequent model updates between clients and the central server. Existing solutions, such as compressing gradients, struggle to balance efficiency and accuracy, particularly in resource-constrained edge environments. There is a growing need for trustworthy, communication-efficient FL solutions that ensure privacy, fairness, and security. To address this, we propose a lightweight novel Hierarchical Federated Learning (HFL) framework that integrates adaptive model pruning, quantization, and model communication and aggregation frequency optimization. First, we introduce a joint model pruning and quantization approach that dynamically adjusts pruning ratios and quantization levels, reducing communication costs while maintaining high accuracy. Second, we develop a fairness aware Stackelberg game-based model communication frequency optimization mechanism, where clients, edge servers, and the central server collaboratively determine optimal update frequencies to balance overhead and convergence speed. Third, we enhance privacy protection using Selective Homomorphic Encryption (SHE) and introduce a verifiable model trust assessment to ensure secure participation of edge devices. Extensive experiments validate our framework’s effectiveness in optimizing communication efficiency, improving model accuracy, and ensuring fast convergence under diverse FL scenarios. The results demonstrate that our approach outperforms state-of-the-art methods in terms of reducing communication overhead, enhancing privacy, and achieving robust learning across varying network and computational constraints.