Hierarchical Federated Learning (HFL) improves the scalability and communication efficiency of the system and achieves load balancing at each level. Incentive mechanisms enhance participant motivation and optimize resource allocation for HFL. However, existing mechanisms mainly focus on maximizing individual utility from the quantity of client data while neglecting to optimize social utility from the learning quality perspective. Meanwhile, strategic behavior and heterogeneous devices can significantly degrade the performance of incentive mechanisms. To this end, we propose a quality-aware incentive mechanism (QAIM) for HFL to improve training efficiency. Specifically, we first systematically evaluate the learning quality of clients based on their training loss and historical records, which allows us to recruit high-quality clients for model updating selectively. Then, we model the cloud-edge-end interaction and cooperation as a three-layer Stackelberg game to analyze the strategies of participants and utilize carefully designed algorithms to derive the solution of the unique Stackelberg Equilibrium (SE). Through the Pareto improvement of client association modeled as a coalition game, we can maximize social utility. Experimental results on both synthetic and real-world datasets demonstrate that our QAIM outperforms the state-of-the-art baselines, with an average increase in accuracy and social utility of 17% and 45%, respectively.

In today’s fast-paced and ever-changing environment, the need for algorithms with enhanced global optimization capability has become increasingly crucial due to the emergence of a wide range of optimization problems. To tackle this issue, we present a new algorithm called Random Particle Swarm Optimization (RPSO) based on cosine similarity. RPSO is evaluated using both the IEEE Congress on Evolutionary Computation (CEC) 2022 test dataset and Convolutional Neural Network (CNN) classification experiments. The RPSO algorithm builds upon the traditional PSO algorithm by incorporating several key enhancements. Firstly, the parameter selection is adapted and a mechanism called Random Contrastive Interaction (RCI) is introduced. This mechanism fosters information exchange among particles, thereby improving the ability of the algorithm to explore the search space more effectively. Secondly, quadratic interpolation (QI) is incorporated to boost the local search efficiency of the algorithm. RPSO utilizes cosine similarity for the selection of both QI and RCI, dynamically updating population information to steer the algorithm towards optimal solutions. In the evaluation using the CEC 2022 test dataset, RPSO is compared with recent variations of Particle Swarm Optimization (PSO) and top algorithms in the CEC community. The results highlight the strong competitiveness and advantages of RPSO, validating its effectiveness in tackling global optimization tasks. Additionally, in the classification experiments with optimizing CNNs for medical images, RPSO demonstrated stability and accuracy comparable to other algorithms and variants. This further confirms the value and utility of RPSO in improving the performance of CNN classification tasks.

The deployment of 6G base stations has brought convenience to the lives of people, but effective implementation in rural areas still faces significant obstacles, making the requirements for base station deployment more demanding. This paper first models the rural base station deployment environment and introduces three objective functions: the effective area of base station deployment, signal security, and uniformity. A weighted multi-objective function is also constructed. Additionally, a Group Merging Particle Swarm Optimization Algorithm (GMPSO) is proposed, incorporating the concept of splitting and perturbation terms into the particle swarm optimization to enhance the optimization capability of the algorithm. Experiments were conducted in both fixed and random environments, comparing various PSO variants from recent years. The impact of different numbers of base stations and clustering centers was also analyzed. The results indicate that GMPSO exhibits significant advantages in base station layout, achieving over 90% coverage and success rates even with fewer base stations, demonstrating the practicality of GMPSO.

Due to the sensitivity of data, Federated Learning (FL) is employed to enable distributed machine learning while safeguarding data privacy and accommodating the requirements of various devices. However, in the context of semidecentralized FL, clients’ communication and training states are dynamic. This variability arises from local training fluctuations, heterogeneous data distributions, and intermittent client participation. Most existing studies primarily focus on stable client states, neglecting the dynamic challenges inherent in real-world scenarios. To tackle this issue, we propose a TRust-Aware clIent scheduLing mechanism called TRAIL, which assesses client states and contributions, enhancing model training efficiency through selective client participation. We focus on a semi-decentralized FL framework where edge servers and clients train a shared global model using unreliable intra-cluster model aggregation and inter-cluster model consensus. First, we propose an adaptive hidden semi-Markov model to estimate clients’ communication states and contributions. Next, we address a client-server association optimization problem to minimize global training loss. Using convergence analysis, we propose a greedy client scheduling algorithm. Finally, our experiments conducted on real-world datasets demonstrate that TRAIL outperforms state-of-the-art baselines, achieving an improvement of 8.7% in test accuracy and a reduction of 15.3% in training loss.

Unmanned aerial vehicles (UAVs) can be utilized as airborne base stations to deliver wireless communication and federated learning (FL) training services for ground vehicles. However, most existing studies assume that vehicles (clients) and UAVs (model owners) offer services voluntarily. In reality, participants (FL clients and model owners) are selfish and will not engage in training without compensation. Meanwhile, due to the heterogeneity of participants and the presence of free-riders and Byzantine behaviors, the quality of vehicles’ model updates can vary significantly. To incentivize participants to engage in model training and ensure reliable outcomes, this paper designs a reliable incentive mechanism (FedBeam) based on game theory. Specifically, we model the cooperation problem between model owners and clients as a two-layer Stackelberg game and prove the existence and uniqueness of the Stackelberg equilibrium (SE). For the cooperation among model owners, we formulate the problem as a coalition game and based on this, analyze and design a coalition formation algorithm to derive the Pareto optimal social utility. Additionally, to achieve reliable FL model updates, we design a weighted-beta (Wbeta) reputation update mechanism to incentivize FL clients to provide high-quality model updates. The experimental results show that compared to the baselines, the proposed incentive mechanism improves social welfare by 17.6% and test accuracy by 5.5% on simulated and real datasets, respectively.