Mobility-Aware and Privacy-Preserving Federated Reinforcement Learning with Multi-Paradigm Machine Learning for Edge Intelligence in 5G/6G Networks

Cluster-Aware Over-the-Air Federated Learning with Energy-Harvesting Devices: From Global Training to Model Personalization

Over-the-air FL with EH MDs under heterogeneous data distributions under heterogeneous data distributions is studied, and the proposed unified framework improves fairness or personalization, depending on the operating mode, while reducing communication overhead.

A hierarchical federated learning framework with FedNova, game-theoretic matching, and QKD-assisted privacy for the internet of vehicles

The Internet of Vehicles (IoV) supports essential intelligent transportation applications but encounters challenges in federated learning (FL) due to non-independent and identically distributed (non-IID) data, vehicle mobility, resource heterogeneity, and strict privacy requirements in latency-sensitive scenarios such as misbehavior detection and accident response. Traditional FL methods, such as random client selection and standard FedAvg, often experience slow convergence and reduced performance under non-IID conditions. We introduce a hierarchical federated learning framework for software-defined vehicular fog computing. The framework incorporates FedNova (a normalized-averaging aggregation method for heterogeneous federated optimization) to produce normalized model updates under data heterogeneity, a Reward-Based Payoff Strategy (RBPS) for incentive-aware client selection, and game-theoretic vehicle-aggregator matching based on the college admissions problem. Privacy is strengthened through quantum key distribution (QKD)-assisted secure key establishment and classical gradient masking, with quantum circuit simulation used to assess future enhancements. The three-layer architecture includes vehicles, Roadside Unit (RSU)/ Base Station (BS)-level aggregators, and a Software-Defined Network Controller (SDNC) global aggregator. The framework uses both monetary and service-based incentives, such as toll exemptions, to encourage vehicle participation. Hybrid simulations using OMNeT++, Veins, SUMO, and the VeReMi misbehavior detection dataset show that the proposed approach achieves 94.8% classification accuracy [95% Confidence Interval (CI): 92.7–97.0 over 10 runs], converges in 120 rounds (33% faster than FedAvg), and reduces average latency by 29% (320 ms compared to 450 ms for FedAvg), with statistically significant improvements (p < 0.05). These gains enable faster model adaptation to evolving attacks (5–10 min shorter training cycles) and support real-time safety applications where delays above 400 ms can compromise road safety. Ablation studies confirm the complementary roles of FedNova, RBPS, and matching. Although quantum operations are currently simulated classically, the design remains compatible with future quantum hardware.

FedAli-PHAR: personalized federated learning with optimal transport-based prototype alignment for robust human activity recognition in pervasive computing

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.

The GAO-based federated learning framework with adaptive client selection for resource-efficient edge-IoT systems

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.

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.