A Simulation-Driven Trust-Aware Federated Learning Framework for Robust Intelligent IoT Networks

Abstract

Federated learning (FL) has emerged as a promising paradigm for enabling distributed intelligence in Internet of Things (IoT) environments while preserving data privacy and reducing the need for centralized data collection. However, the practical deployment of FL in IoT scenarios remains challenging due to heterogeneous data distributions, unreliable communication conditions, and the presence of faulty or malicious edge devices that can disrupt collaborative training. These limitations can significantly degrade convergence stability and predictive performance, particularly in resource-constrained and intermittently connected networks. This paper proposes a simulation-driven trust-aware federated learning framework for robust intelligent IoT networks. The proposed approach incorporates a dynamic trust-based aggregation mechanism that adaptively weights client contributions based on the consistency of their local model updates with the global model state. In addition, a controlled IoT-oriented federated simulation environment is developed to emulate heterogeneous edge conditions, including non-independent and identically distributed (non-IID) data partitioning, adversarial model manipulation, and intermittent client connectivity caused by communication dropouts. Extensive multi-seed experiments were conducted on the UCI Human Activity Recognition (UCI HAR) dataset and complemented with an auxiliary CIFAR-10 convolutional neural network (CNN) validation scenario. The evaluation considered multiple adversarial settings, including sign-flip, Gaussian-noise, scaling, and label-flip attacks, as well as communication-dropout probabilities up to 50%. In contrast with the initial FedAvg-only evaluation, the revised experimental analysis includes comparisons with representative robust aggregation baselines, namely Median, Trimmed Mean, Krum, Multi-Krum, and an auxiliary Bulyan configuration. The experimental results demonstrate that the proposed Trust-FedAvg framework substantially improves robustness over conventional FedAvg and remains competitive with established robust aggregation strategies, particularly under directional model-manipulation attacks and intermittent-connectivity conditions. Under a 20% sign-flip attack on UCI HAR, the proposed method achieved a final test accuracy of 86.2%, whereas conventional FedAvg degraded to approximately 44.7%. Furthermore, under combined adversarial and intermittent-connectivity conditions with 50% communication dropout, Trust-FedAvg maintained a final accuracy of 57.0%, compared with 21.3% for FedAvg, 24.8% for Median, and 14.6% for Trimmed Mean. The additional experiments also show that Trust-FedAvg is not universally superior across all perturbation types: under severe Gaussian-noise attacks, coordinate-wise Median and Multi-Krum provided stronger robustness in some settings. Overall, the results suggest that trust-aware aggregation can improve robustness against unreliable or malicious simulated clients while preserving a relatively simple aggregation procedure. Runtime measurements further indicate that the proposed method introduces only limited round-level overhead compared with FedAvg, while remaining simpler than more complex Byzantine-resilient alternatives. Further validation with real IoT deployments, additional sensor datasets, asynchronous communication models, energy profiling, and communication-overhead measurements is required to fully assess deployment feasibility in real IoT environments. The proposed framework provides a practical, extensible basis for the design and evaluation of resilient AI-enabled IoT networks operating under controlled but practically relevant edge-learning constraints.