TL;DR
Results demonstrate the practicality of integrating predictive intelligence and decentralized coordination for scalable and secure FL, and avoids overloading any single node and ensures fault-tolerant, adaptive selection through continuous monitoring and helper-assisted data gathering.
Federated learning (FL) provides a privacy-preserving paradigm for distributed model training in Internet-of-Things (IoT) systems, but it faces challenges from non-IID data heterogeneity and adversarial threats. Although the Shapley value provides a principled framework for client contribution evaluation, its exponential complexity renders it impractical for resource-constrained IoT devices. This paper proposes a Lightweight Bandit-Shapley Federated Learning (LBSFL), a lightweight trust-aware FL framework tailored for IoT environments. A truncated Monte Carlo method efficiently approximates client contributions under limited computational budgets. A multi-armed bandit mechanism balances exploration and exploitation during client selection, thereby reducing communication overhead. Contribution-guided aggregation weights suppress unreliable updates while prioritizing high-quality clients. Experiments under heterogeneous data distributions and adversarial scenarios show that LBSFL achieves competitive model accuracy while substantially reducing computational and communication overhead in most evaluated settings. These results indicate that LBSFL provides a favorable trade-off between robustness and efficiency for IoT-oriented federated learning.
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.
The proliferation of Internet of Things (IoT) devices has generated unprecedented volumes of data, rendering traditional cloud-centric processing paradigms inadequate due to latency constraints, bandwidth limitations, and privacy concerns. This paper presents a comprehensive mathematical framework for distributed intelligence at the edge, enabling decentralized learning across heterogeneous IoT networks. We propose a novel Federated Edge Learning (FEL) architecture that integrates software-defined networking principles with gossip-based communication protocols to facilitate collaborative model training while preserving data locality. The framework addresses critical challenges including device heterogeneity, non-independent and identically distributed (non-i.i.d.) data distributions, resource constraints, and communication efficiency. We formalize the decentralized learning problem, derive convergence bounds under heterogeneous conditions, and introduce a multi-worker selection mechanism optimized through swarm learning principles. Experimental validation using real-world IoT datasets demonstrates that our approach achieves 30-50% reduction in training latency and 35-55% decrease in energy consumption compared to conventional federated averaging methods, while maintaining competitive accuracy of 92.86% on classification tasks . The proposed framework offers a scalable, privacy-preserving solution for deploying artificial intelligence at the network edge.
Smart cities, intelligent transportation systems, and industrial infrastructures increasingly rely on IoT, edge computing, and AI to enable real-time monitoring and predictive maintenance. However, centralized machine learning raises concerns regarding data privacy, communication overhead, security, and data ownership. This paper proposes a Federated Learning Framework for Privacy-Preserving Smart Infrastructure Monitoring (FL-PSIM), which enables decentralized model training without transferring raw infrastructure data. Edge devices collaboratively share encrypted model updates using secure aggregation, differential privacy, and adaptive encryption techniques to preserve confidentiality. The framework also incorporates edge-cloud collaboration to balance computational efficiency, model accuracy, and network resource utilization. Designed to support heterogeneous sensor environments across transportation, energy, industrial, and urban systems, FL-PSIM optimizes global learning while maintaining local data privacy. Experimental results demonstrate improved monitoring accuracy, anomaly detection, communication efficiency, scalability, and resilience against cyber threats compared with centralized AI approaches. The proposed framework provides a secure, privacy-preserving, and scalable foundation for next-generation smart infrastructure, supporting sustainable digital transformation, smart cities, and Industry 5.0 applications.
The Federated Green Anaconda Optimizer (FedGAO), an innovative FL framework inspired by the behavioral patterns of the Green Anaconda Optimizer (GAO), is proposed, demonstrating superior performance in terms of accuracy, convergence speed, and resource efficiency.
Internet of Things (IoT), edge computing, and cloud computing has transformed data-driven services across healthcare, transportation, manufacturing, finance, agriculture, and smart cities. These applications generate large volumes of sensitive distributed data, making traditional centralized machine learning unsuitable due to privacy, security, communication, and regulatory challenges. Federated Learning (FL) addresses these issues by enabling collaborative model training without transferring raw data, thereby preserving user privacy.This paper presents a privacy-preserving federated learning framework that integrates secure aggregation, differential privacy, encryption, adaptive communication, and federated optimization for large-scale heterogeneous environments. The framework incorporates edge computing, blockchain, Trusted Execution Environments (TEE), and Explainable AI (XAI) to improve security, transparency, and trust. A multi-layer architecture consisting of client devices, edge servers, federated coordinators, cloud services, and security modules is proposed. Adaptive client selection, weighted federated averaging, dynamic privacy allocation, and asynchronous synchronization are employed to improve learning under Non-IID data distributions. Experimental results demonstrate high model accuracy, reduced privacy leakage, lower communication overhead, faster convergence, enhanced scalability, and strong resilience against security attacks, making the proposed framework suitable for next-generation privacy-preserving smart applications.