Trust- and Energy-Aware Federated Learning for Wireless Sensor Networks: A Lightweight Orchestration Framework for Heterogeneous IoT Environments

Wireless Sensor Networks (WSNs) are increasingly evolving toward intelligent distributed systems in which local sensing, on-device inference, and collaborative model training are becoming central to scalable Internet of Things (IoT) deployments. However, the practical adoption of Federated Learning (FL) in WSN-oriented environments remains constrained by three major challenges: limited and unevenly depleted node energy, heterogeneous non-IID local data distributions, and variable client reliability during collaborative training. This paper proposes a Trust- and Energy-Aware Federated Learning (TEA-FL) framework specifically designed for resource-constrained WSN settings, in which client participation and server-side aggregation are jointly guided by residual energy estimates and dynamically updated trust scores. The proposed method prioritizes reliable, energy-efficient sensor nodes while reducing the impact of weakly aligned or low-quality local updates during global aggregation. The framework is evaluated on two representative WSN/IoT-oriented proxy benchmarks, Human Activity Recognition (HAR) and UNSW-NB15 intrusion detection, under both IID and Dirichlet-based non-IID federated partitions. Under non-IID HAR partitioning, TEA-FL improved final accuracy from 0.6752 with FedAvg to 0.7636 and final Macro-F1 from 0.5623 to 0.7185. On the more challenging non-IID UNSW-NB15 benchmark, TEA-FL achieved the highest final Macro-F1, 0.3711, compared with 0.3230 for FedAvg and 0.3323 for the trust-only baseline, although with a lower final accuracy. These results indicate that TEA-FL is particularly useful when final-round robustness, class-balanced behavior, and client sustainability are more relevant than maximizing a single peak intermediate accuracy value. Additional ablation and unreliable-client experiments further show that the trust–energy-aware aggregation component is particularly influential and that TEA-FL can improve behavior under selected low-quality participation scenarios, although it should not be interpreted as a complete Byzantine-robust defense. Overall, the findings suggest that jointly modeling update consistency and residual energy offers a practical, lightweight pathway toward more dependable and sustainable federated intelligence in next-generation WSN and IoT deployments.