Toward Fusion Intelligence of Open Radio Access Network with Federated Learning: A Survey

DISTRIBUTED INTELLIGENCE AT THE EDGE: A MATHEMATICAL FRAMEWORK FOR DECENTRALIZED LEARNING IN IOT NETWORKS

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.

Federated Learning over 5G/6G Networks: Dynamic Client Selection and Resource Allocation for Heterogeneous Edge Environments

Results support the central conclusion that lightweight joint scheduling can materially improve wall-clock FL efficiency in heterogeneous 5G/6G edge networks.

Communication-Aware Federated Learning for Energy Management in Edge-Cloud Autonomous Systems

Machine Learning-Enabled Edge Intelligence for IoT Communication Systems: A Structured Review

This structured review investigates how machine learning-enabled edge intelligence can improve the performance, efficiency, and resilience of Internet of Things communication systems under constraints of latency, bandwidth, energy, privacy, and device heterogeneity. A structured literature review was conducted using peer-reviewed studies from major academic databases, with the selected work classified according to learning paradigms, edge deployment strategies, communication functions, application domains, evaluation metrics, and practical limitations. The synthesis shows that supervised learning, deep learning, reinforcement learning, federated learning, lightweight model compression, and Tiny Machine Learning can support adaptive scheduling, intelligent routing, computation offloading, bandwidth allocation, anomaly detection, and privacy-preserving collaboration across device-edge-cloud architectures. The reviewed evidence indicates that these approaches can reduce end-to-end delay, communication traffic, and energy consumption while improving resource utilization, local autonomy, and responsiveness in smart cities, industrial systems, healthcare, agriculture, and intelligent transportation. However, performance gains remain strongly dependent on communication conditions, model size, data distribution, hardware capability, synchronization overhead, and security requirements. The review further identifies unresolved challenges involving non-independent and identically distributed data, unstable wireless links, model adaptability, privacy leakage, adversarial threats, and the absence of standardized multi-objective benchmarks. It concludes that future Internet of Things systems should adopt communication-computation-learning co-design, lightweight and adaptive models, privacy-aware distributed intelligence, and cross-layer orchestration to achieve scalable, trustworthy, and energy-efficient edge intelligence.

Hybrid optimization framework of federated learning of multi-models in edge networks: trade-off of energy, latency, and fairness

Federated Learning in Distributed Cloud Systems: Enhancing Privacy and Scalability for Machine Learning in Edge Computing

In the era of edge computing, where data is generated and processed at the network's edge, ensuring privacy and scalability in machine learning models is paramount. Federated Learning (FL) addresses these challenges by allowing multiple edge devices to collaboratively train models without sharing raw data. This paper investigates the implementation of FL in distributed cloud systems, highlighting its role in preserving data privacy and improving scalability. We analyze various FL algorithms, such as Federated Averaging (FedAvg) and Hybrid Federated Dual Coordinate Ascent (HyFDCA), assessing their effectiveness in edge computing contexts. Additionally, we explore techniques like inverse distance aggregation to handle non-IID data distributions and discuss the trade-offs between communication and computation in FL frameworks. Through comprehensive analysis and experimentation, this study provides insights into optimizing FL for edge computing, paving the way for more secure and scalable machine learning applications in distributed cloud environments.