Federated learning (FL) can effectively address the phenomenon of data silos. It allows multiple clients to jointly train machine learning models and protect their local data confidentiality. However, it is a key challenge to stimulate the enthusiasm of these clients, ensure their willingness to invest in model training, and ensure that the model can efficiently reach a convergence state and maintain a high level of testing accuracy. This article proposes an uncertainty-aware auction mechanism (UAMTRA) that considers the training time, client reputation, and Age of Update (AoU). Firstly, client reputation is introduced to mirror truthfully the client's data quality and training status. AoU is introduced to measure the interval required for the server to receive the latest updates from the client to explore better and utilize the client's data resources and avoid overfitting. Subsequently, we reformulate the objective of maximizing social welfare as an NP-hard problem. To resolve this problem, we introduce the UAMTRA mechanism, which is capable of approaching maximal social welfare while ensuring minimal time complexity. In addition, UAMTRA also considers the differences in client network environments and heterogeneity of data resources, which can fully exploit and utilize client data resources to promote rapid convergence of FL and achieve higher testing accuracy. Ultimately, the efficacy of UAMTRA was confirmed through simulated experiments. Compared to other mechanisms, UAMTRA exhibited quicker convergence rates and superior testing accuracy on the MNIST datasets.

Client selection is a promising approach to address heterogeneity in Federated learning. Existing methods predominantly focus on optimizing training efficiency, often neglecting the impact of selection fairness. In this paper, we investigate client selection in volatile environments, leveraging the Age-of-Information concept to formulate long-term fairness constraints and develop an optimization model that ensures fairness while maximizing overall efficiency. By converting the offline problem into an online framework via Lyapunov optimization, we reframe client selection under the Contextual Combinatorial Multi-Armed Bandit (C2MAB) framework. Integrated with the LinUCB algorithm, we propose FedECS, an efficient client selection strategy. Experiments on public datasets demonstrate FedECS’s superior fairness guarantees, achieving 13% higher accuracy than FedCS and 5.8% improvement over RBCS-F.

Federated Learning (FL) is a distributed machine learning paradigm that trains global model parameters by aggregating model parameters from distributed clients, offering good privacy protection due to its distributed training characteristics. Additionally, due to distributed training, the heterogeneity between different clients can lead to instability in the global model training process. Therefore, it is necessary to select the right clients to participate in the global training. At the same time, since the local computing resources and battery power of most distributed clients are less than those of servers, from the perspective of reducing the long-term training cost of each client, it is beneficial for clients participating in the global model parameter update to be distributed across multiple clients. This paper proposes a new FL client selection method (Fed-LyAHP) based on the Analytic Hierarchy Process (AHP) and Lyapunov optimization, which can not only balance model accuracy and training time in the client selection process, but also introduce virtual queues to reduce the probability of a single client participating in the global update all the time. Experiments show that Fed-LyAHP is superior to Greedy methods, RBCS F and FedCS, in terms of training time, model accuracy, and queue length. Experiments on two different datasets show that Fed-LyAHP outperforms other algorithms by at least 10% in terms of queue length. In the fixed accuracy experiment, only 70% of the iterations of other algorithms were used to achieve the target accuracy. In experiments with fixed run-time, the accuracy can be improved by at least 10%.

A federated learning framework enables multiple clients to jointly train models locally without uploading their private data, effectively protecting the clients’ data privacy. However, existing federated learning auction mechanisms have not considered heterogeneity in client training time, making it difficult for the server to aggregate client models effectively within a constrained time. Moreover, continuously selecting specific clients in each round can lead to overfitting. This article proposes an Uncertainty-aware Auction Mechanism (UAMARD) based on Age of Update (AoU), Reputation, and Data Quantity, which considers training time and provides guidance on the number of data points to participate in training for selected clients. Firstly, we model a reverse auction system that considers the uncertainty of training time to promote client participation. We introduce AoU to quantify the time interval required for the server to receive the latest updates from the client to avoid overfitting. Then, we prove that solving the problem of maximizing social welfare is NP-hard. Subsequently, we introduce a dynamic programming algorithm (VCG RA) to solve the problem of maximizing social welfare. To further reduce time complexity, we propose our UAMARD method, which achieves a near-optimal level of social welfare while ensuring minimal time complexity. Ultimately, simulation experiments confirmed the efficacy of UAMARD and VCG RA. When benchmarked against other mechanisms, UAMARD and VCG RA demonstrated superior performance with quicker convergence and higher accuracy in testing the MNIST and CIFAR-10 datasets.

Abstract Federated learning represents a decentralized approach to machine learning, enabling numerous devices to collaboratively contribute to model training while ensuring the privacy of individual data. However, the existing incentive mechanism of hierarchical federated learning (HFL) only considers the data contribution of a single round, which needs to be revised. For non‐IID data sets, the continuous selection of any end devices will cause the weights to diverge in a specific direction. Therefore, a new metric is needed to avoid continuously selecting a certain end device to ensure the overall effectiveness. We introduce a metric to describe the importance of updates: age of update (AoU), which can help select end devices not selected in the previous round to promote a faster model convergence. We put forward an incentive mechanism based on AoU, reputation, and data quantity in HFL (ARDHFL). We have derived the optimal equilibrium solution for the three‐stage Stackelberg game. Based on this solution, we can ensure maximum edge‐cloud utility while incentivizing end devices to engage actively in HFL tasks and providing superior data to train the HFL model. Finally, we conducted extensive experiments to prove that ARDHFL can effectively improve the performance. Compared with the fixed scheme, random scheme, FMore and InFEDge, the testing accuracy of ARDHFL in the MNIST dataset has been improved by 29.7%, 9.3%, 6.8% and 6.1%, respectively. In the CIFAR‐10 dataset, it has been improved by 40.2%, 33.1%, 16.4% and 14.2%, respectively, and demands fewer communication iterations to achieve the same testing accuracy.

Federated Learning (FL) is a new distributed machine learning that allows end devices to collaboratively train a global model using local data to protect data privacy. However, the existing FL incentive mechanisms only consider a single round’s data contribution, which needs improvement. For non-IID datasets, continuous selection of any end device will cause weights to diverge in a specific direction. Therefore, a new metric is needed to avoid continuously selecting a particular end device to ensure the overall performance of the incentive mechanism. We have introduced a metric to describe the importance of updates: Age of Update (AoU), which can help select end devices not selected in the previous round to promote faster model convergence. We proposed an incentive mechanism for a Two-stage Stackelberg Game with Multidimensional Individual Attributes (TSGMIA). We have derived the optimal equilibrium solution for the game in two stages. Based on this solution, we can ensure maximum server utility while incentivizing clients to participate actively in FL tasks and providing high-quality data to train the FL model. Finally, we conducted extensive simulation experiments to demonstrate that our proposed mechanism can effectively improve the performance of the FL model. Specifically, when the model accuracy reaches 0.9, compared to InFEDge, the communication round of TSGMIA will be reduced by 9%. In addition, compared with the fixed selection scheme, random selection scheme, and InFEDge, after 100 rounds of training, TSGMIA improved the accuracy of the global model by 30%, 10%, and 1.7%, respectively.

Federated learning (FL) is a distributed learning paradigm that enables multiple client devices to collaboratively train a global model based on their local datasets while protecting data privacy. However, due to its distributed nature, FL is susceptible to the resources of heterogeneous client devices with different data quantities, communication resources, and computing capabilities. Heterogeneity leads to uncertain global model training time and hinders the convergence of the global model. Therefore, selecting suitable clients to participate in the FL training process is necessary to improve the efficiency of FL. This paper proposes an FL client selection method (FedAHP) based on the Analytic Hierarchy Process (AHP) to optimally balance the trade-off between model accuracy and training time during client selection. Experiments show that FedAHP outperforms the greedy method regarding training time consumption and model accuracy. Specifically, FedAHP achieves a 67% reduction in communication rounds compared to the greedy method when the model accuracy reaches 0.90. Furthermore, when taking the same 20 hours, FedAHP improves the accuracy of the global model by 6% in comparison to the greedy method.

Recommendation systems powered by artificial intelligence (AI) are widely used to improve user experience. However, AI inevitably raises privacy leakage and other security issues due to the utilization of extensive user data. Addressing these challenges can protect users’ personal information, benefit service providers, and foster service ecosystems. Presently, numerous techniques based on differential privacy have been proposed to solve this problem. However, existing solutions encounter issues such as inadequate data utilization and a tenuous trade-off between privacy protection and recommendation effectiveness. To enhance recommendation accuracy and protect users’ private data, we propose ID-SR, a novel privacy-preserving social recommendation scheme for trustworthy AI based on the infinite divisibility of Laplace distribution. We first introduce a novel recommendation method adopted in ID-SR, which is established based on matrix factorization with a newly designed social regularization term for improving recommendation effectiveness. We then propose a differential privacy-preserving scheme tailored to the above method that leverages the Laplace distribution’s characteristics to safeguard user data. Theoretical analysis and experimentation evaluation on two publicly available datasets demonstrate that our scheme achieves a superior balance between privacy protection and recommendation effectiveness, ultimately delivering an enhanced user experience.

Federated Mobile CrowdSensing is applied to collect massive sensory data and exploits the computing power of mobile devices brought by their embedded specialized computing engines (e.g., Neural Engine in iPhone) to train machine learning (ML) models. However, the heterogeneity of mobile devices includes significant differences in the size and quality of datasets, different computing power, and some unreliable clients using unreliable data for training. The heterogeneity of mobile devices reduces FL’s performance. Therefore, selecting high-quality clients for Federated learning (FL) is vital. This study proposes a client selection scheme based on the reverse auction. First, each client’s training time is predicted, the total FL time threshold is optimized, and the reputation value is calculated based on the historical performance of each client. Then, each client’s current computing power and dataset size are converted into an efficiency value. Finally, the selection value of each client is calculated based on the efficiency value and reputation value. The results of the experiments show that our scheme can select high-quality clients. Compared with FedRep, our scheme can reduce training time by 91.5%. Compared with FedEff, our scheme can reduce communication rounds by 87.5%. In the same communication rounds (5000), our scheme has higher accuracy than RandomFL, and the average accuracy is improved by about 4.4%.

Spotting graphical symbols from the computer-aided design (CAD) drawings is essential to many industrial applications. Different from raster images, CAD drawings are vector graphics consisting of geometric primitives such as segments, arcs, and circles. By treating each CAD drawing as a graph, we propose a novel graph attention network GAT-CADNet to solve the panoptic symbol spotting problem: vertex features derived from the GAT branch are mapped to semantic labels, while their attention scores are cascaded and mapped to instance prediction. Our key contributions are three-fold: 1) the instance symbol spotting task is formulated as a subgraph detection problem and solved by predicting the adjacency matrix; 2) a relative spatial encoding (RSE) module explicitly encodes the relative positional and geometric relation among vertices to enhance the vertex attention; 3) a cascaded edge encoding (CEE) module extracts vertex attentions from multiple stages of GAT and treats them as edge encoding to predict the adjacency matrix. The proposed GAT-CADNet is intuitive yet effective and manages to solve the panoptic symbol spotting problem in one consolidated network. Extensive experiments and ablation studies on the public benchmark show that our graph-based approach surpasses existing state-of-the-art methods by a large margin.

Mobile Crowd Sensing (MCS) typically assigns sensing tasks in the same target area to many participants considering data quality and the diversity of sensing devices. However, participant selection is based on the individual in many research. The efficiency of individual recruitment is low. Individuals need higher transportation costs to go to the task location alone, and the data quality perceived by individuals is difficult to guarantee. This paper proposes a team-based multitask data acquisition scheme under time constraints to address these challenges. The scheme optimised the number of participants, traffic cost, and data quality and designed four team-based multitask allocation algorithms under time constraints in the MCS: T-RandomTeam, T-MostTeam, T-RandomMITeam, and T-MostMITeam. The team size is associated with the number of participants required for the first task or the vehicle capacity to perform the task. We conducted extensive experiments based on a real large-scale dataset to evaluate the four algorithms' performances compared to two baseline algorithms (T-Random and T-most). The efficiency of the four algorithms has been significantly improved by team recruitment. The transportation cost can be multiplicatively reduced by carpooling. Data quality can be improved by at least 2% through reputation screening and team members' communication.

Federated learning (FL) plays an important role in the development of smart cities. With the evolution of big data and artificial intelligence, issues related to data privacy and protection have emerged, which can be solved by FL. In this paper, the current developments in FL and its applications in various fields are reviewed. With a comprehensive investigation, the latest research on the application of FL is discussed for various fields in smart cities. We explain the current developments in FL in fields, such as the Internet of Things (IoT), transportation, communications, finance, and medicine. First, we introduce the background, definition, and key technologies of FL. Then, we review key applications and the latest results. Finally, we discuss the future applications and research directions of FL in smart cities.