Adaptive federated edge intelligence with semantic communication and trust-aware optimization for heterogeneous IoT networks
The rapid growth of heterogeneous Internet of Things (IoT) networks introduces significant challenges in communication efficiency, energy consumption, and reliable distributed intelligence due to diverse device capabilities, dynamic channel conditions, and limited computational resources. Existing bit-level communication and conventional federated learning approaches often suffer from excessive communication overhead, inefficient resource utilization, and unstable model convergence under non-IID data and unreliable device participation. To address these limitations, this paper proposes FEI-SemCom, an adaptive federated edge intelligence framework that integrates semantic communication, federated learning, and joint resource optimization for heterogeneous IoT systems. The proposed framework introduces a heterogeneity-aware semantic encoder, adaptive semantic compression mechanism, semantic contribution-based client selection strategy, and trust-aware aggregation scheme to improve communication efficiency and learning robustness. Unlike existing approaches that optimize communication, learning, and resource allocation independently, FEI-SemCom jointly adapts semantic representation, client participation, transmission parameters, and aggregation weights according to device capability, channel quality, and energy availability. Furthermore, the proposed framework improves resilience under intermittent connectivity conditions by enabling adaptive client participation, reliability-aware aggregation, and resource-aware communication decisions, allowing federated edge intelligence to operate effectively despite temporary link failures and unstable device availability. Extensive simulation results demonstrate that the proposed framework achieves 91.2% accuracy at 5 dB signal-to-noise ratio (SNR), converges within approximately 120 training rounds, and reduces communication overhead, energy consumption, and latency to 45 MB, 48 J, and 110 ms, respectively. Compared with conventional approaches, FEI-SemCom reduces communication overhead by up to 38% while maintaining stable convergence and robust learning performance. These results demonstrate the potential of adaptive semantic communication combined with federated edge intelligence for scalable and resource-efficient IoT applications.