Jul 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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 is proposed, offering a scalable, privacy-preserving solution for deploying artificial intelligence at the network edge.
Abstract
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.
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.
M. Reis, Carlos Serôdio, Frederico Branco· Applied Sciences· 0 citations
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.
Mohamed Aiche, Samir Ouchani, Hafida Bouarfa· Innovations in Systems and S...· 0 citations
The intersection of sixth-generation (6G) communication networks, edge computing, and federated learning offers a novel occasion regarding empowering trustful and scalable collaboration throughout distributed Internet of Things (IoT) ecosystems. Conventional centralized machine learning technology is plagued by severe constraints in IoT scenes, such as privacy issues, network congestion, and network bottlenecks. The paper has presented a new 6G-integrated federated learning system which builds on the native intelligence of 6G networks to support secure, efficient, and scalable cooperative learning among heterogeneous edge-IoT devices. The suggested architecture combines terahertz frequencies to synchronize model communication in real-time at ultra-low latency, reconfigurable intelligent surfaces to improve the quality of communication, and network slicing to provide differentiated quality-of-service assurances. Another new hierarchical federated learning system integrates intra-edge aggregation and inter-edge cooperation that can reduce communication overhead by 85-percent and achieve the same accuracy in models. This framework integrates blockchain-based trust management involving zero-knowledge proofs of verifiable model update, to provide integrity and accountability without impacting on privacy. Experimental analysis of massive scale edge-IoT applications has revealed that the suggested scheme attains 97.2% model precision and lowers communication expenses by 87 percent and convergence rate by 3.4 times that of traditional federated learning techniques. The framework has high-uniform performance in adversarial environments where 99.6 percent of malicious model updates are identified with a small false positive. The results define the 6G-integrated federated learning as a framework of reliable and scalable edge-IoT cooperation.
T.Muthumanickam, D. Jayalakshmi, Sathiyamoorthy M et al.· 2026 6th International Confe...· 0 citations
This work introduces a federated learning framework on campus that provides a resource-conscious multi-layer federating strategy that controls the number of devices taking part in the process as well as the frequency at which aggregation is done based on each device’s capability.
C. Ravi, S. Reddy, S. Bhargav et al.· International Journal of Ele...· 0 citations
Edge computing enables distributed intelligence in resource-constrained IoT environments. However, traditional Federated Learning (FL) struggles with heterogeneous device capabilities, dynamic network conditions, and non-IID data distributions, resulting in straggler effects, slow convergence, and inefficient resource utilization. This paper proposes Resource-Aware Dynamic Split Federated Learning (RAD-SFL), a framework for efficient distributed training in heterogeneous edge environments. RAD-SFL introduces a dynamic model layer splitting mechanism that adaptively partitions model execution between client devices and edge servers based on real-time computation and communication conditions, and a group-and-reorder technique that organizes devices into balanced groups with similar data distributions to improve model convergence under non-IID settings. We validate RAD-SFL through experiments on widely adopted datasets using both a simulated environment and a real testbed with heterogeneous IoT devices. Results demonstrate that RAD-SFL reduces the training time by up to 66.4%, decreases device-side energy consumption by 52.5%, and improves global model accuracy by up to 30.5% compared to FL and SFL baselines.
Aditya Pribadi Kalapaaking, Veronika Stephanie, Eric Samikwa et al.· International Conference on...· 0 citations
The rapid proliferation of Internet of Things (IoT) devices across domains such as smart transportation, healthcare, and smart city infrastructure has intensified the demand for low-latency, energy-efficient, and scalable computing paradigms. While cloud computing has traditionally served as the backbone for IoT data processing, its inherent limitations have catalyzed the emergence of edge/fog computing, forming a distributed edge-cloud continuum. This review examines emerging trends in Artificial Intelligence (AI)-driven resource management within this continuum, with a focus on three directions: (1) the transition from centralized to distributed and collaborative intelligence, (2) cross-domain adaptation and knowledge transfer for heterogeneous IoT applications, and (3) the nascent integration of foundation models into edge environments. We ground our discussion in three complementary case studies: smart transportation as a representative vertical domain, cross-domain heterogeneous IoT application scheduling as a horizontal perspective, and industrial transferred arc plasma monitoring as an emerging industrial IoT scenario, and conclude with forward-looking research directions, including quantum-enhanced edge optimization, edge-native continual learning, digital twin-driven resource orchestration, and neuromorphic computing for ultra-low-power edge AI.
Zhiyu Wang, Nilotpal Kapri, L. Bittencourt et al.· Frontiers in The Internet of...· 0 citations