Hierarchical Mean-Field Theory-based Off-Policy GRPO for Federated Edge Learning in Resource-Constrained Edge Computing

Coalitional Game-Based Clustered Federated Learning for Mitigating Data Heterogeneity in Wireless Networks

Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving distributed machine learning across mobile and edge devices. Nevertheless, the presence of non-independent and identically distributed (non-IID) data in wireless networks often leads to severe gradient divergence, slow convergence, and degraded model performance. To mitigate these challenges, this paper proposes FedCoalition, a coalition game-based clustered federated learning framework that organizes clients into collaborative coalitions according to the similarity of their model updates. The proposed method integrates a coalition formation mechanism with similarity-aware clustering to reduce gradient divergence among participating clients. In addition, each coalition designates a Coalition Head (CH) responsible for intra-cluster aggregation and model validation using auxiliary data, thereby improving model reliability and communication efficiency. The experimental results on MNIST and CIFAR-10 under non-IID data distributions demonstrate that FedCoalition significantly improves learning performance, achieving higher accuracy and faster convergence compared with existing clustered federated learning approaches.

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

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.

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.

KaaS-Edge: Resource-Aware Knowledge Distillation Service for Heterogeneous Wireless Edge Networks

Federated distillation (FD) enables collaborative edge learning by exchanging soft predictions rather than model parameters, offering communication efficiency and architectural flexibility. However, deploying FD over heterogeneous wireless networks requires principled methods to schedule device participation and allocate upload volumes under per-round resource constraints. Existing approaches assume uniform participation or rely on heuristic selection, ignoring the coupling among communication cost, computational capability, and privacy posture across devices. This paper proposes KaaS-Edge, a Knowledge-as-a-Service framework that formulates device scheduling as budgeted submodular maximization. We derive an optimal water-filling volume allocation in closed form and present RADS (Resource-Aware Distillation Scheduling), a greedy algorithm with a constant-factor approximation guarantee. Experiments on CIFAR-100 demonstrate that KaaS-Edge achieves accuracy comparable to full-participation baselines while reducing per-round communication by nearly ten times and cumulative bandwidth by over an order of magnitude, with graceful degradation under stringent privacy constraints.