Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine learning model, and then send the trained models to the BS, which generates the initial model, collects local models and constructs the global model. Then, we formulate the incentive mechanism between the BS and mobile users as an auction game where the BS is an auctioneer and the mobile users are the sellers. In the proposed game, each mobile user submits its bids according to the minimal energy cost that the mobile users experiences in participating in FL. To decide winners in the auction and maximize social welfare, we propose the primal-dual greedy auction mechanism. The proposed mechanism can guarantee three economic properties, namely, truthfulness, individual rationality and efficiency. Finally, numerical results are shown to demonstrate the performance effectiveness of our proposed mechanism.

With the increasing number of electric vehicles, deploying fixed charging stations (FCSs) has been a widely adopted solution for providing charging services to EVs. However, the charging requirement of EVs being near overload FCSs and/or in areas of inadequate charging infrastructures such as highways and rural areas will surpass the capabilities of FCSs. To address this challenge, the realized peer-to-peer (P2P) energy trading between electric vehicles (EVs) and mobile charging stations (MCSs) can be utilized to relieve the overload on FCSs, leverage under-utilized energy resources among cities and achieve trading benefits. By deploying this energy trading model, EVs purchase available energy from MCSs instead of FCSs to fulfill their charging demands. However, such an energy trade requires an incentive mechanism to ensure fair trading and prevent personal gain, which motivates us to study the incentive mechanism design for the energy trading model. In this paper, first, we consider an energy trading system involving multiple MCSs and EVs. Then, we formulate the incentive mechanism between MCSs and EVs as an auction game, in which the MCSs are auctioneers and EVs are bidders. In the formulated problem, each EV secretly submits its bid to MCSs, and each MCS distributively decides the winners without the knowledge of other MCSs. To achieve this, we design a distributed action-based energy trading mechanism that ensures fairness of providing charging service, validity in trading price’s resource determination, and nothing incentivizing the EVs to cheat on payment decisions. The proposed energy trading scheme achieves certain critical properties, including truthfulness, individual rationality, budget balance, and computational efficiency for both buyers and sellers. Finally, we perform simulation experiments to verify the proposed scheme’s effectiveness compared with the baseline in the literature.

Mobile Edge Computing (MEC) offers a promising solution for reducing the latency of computationally intensive tasks, and is attracting investment from number of companies. Motivated by the shared infrastructure of colocation data centers and the growth of edge computing, colocation MEC businesses have been formed. In colocation MEC, a colocation service provider leases data center spaces at base stations to tenants for housing their servers. Although the tenants fully manage their own servers, they must still purchase bandwidth from the network provider via the bandwidth purchasing market to serve their users' dynamic demand. Each tenant aims to minimize both the delay and the energy consumption which results in a trade-off due to the dependence between bandwidth and server computing speed. black In this paper, an auction framework is used to model the interaction between the network provider and tenants in the bandwidth market. The network provider determines the bandwidth amount and corresponding payment by tenants based on their submitted bids, in which the valuation of the bandwidth amount in each bid is based on the coupling between bandwidth and server computing speed. The networking provider also seeks winners in the social welfare maximization problem, which is NP-hard. To solve this problem, we propose a solution based on the randomized auction mechanism, which is proved to be approximately truthful, individually rational, and computationally efficient. Simulation results verify the effectiveness of our proposed randomized auction scheme in bandwidth trading.

This paper thoroughly reviews face detection techniques, primarily focusing on applying Eigenfaces, a powerful method rooted in Principal Component Analysis (PCA). The goal is to provide a comprehensive understanding of the advancements, challenges, and prospects associated with Eigenface-based face detection systems. The review commences with exploring the comprehensive facial recognition system framework using Eigenfaces and studying the intricacies of employing Eigenfaces as a foundational element for robust facial recognition. Then, we describe the taxonomies of various Eigenface-based face detection approaches to provide a systematic understanding of the diverse strategies utilized in Eigenface-based face detection systems. Besides, the paper explores benchmarking datasets tailored specifically for facial recognition. These datasets are critically analyzed, highlighting their relevance, limitations, and potential impact on developing and assessing Eigenface-based face detection algorithms. Furthermore, the review details the limitations and open issues inherent in Eigenface-based face detection systems. Addressing concerns such as sensitivity to lighting conditions, occlusions, and scalability, this section aims to guide future research directions by identifying gaps in the current understanding and proposing potential avenues for improvement.

Federated learning (FL) has emerged as a promising framework to exploit massive data generated by edge devices in developing a common learning model while preserving the privacy of local data. In implementing FL over wireless networks, the participation of more devices is encouraged to alleviate the training inefficiency due to irregular local data but it tends to increase communication latency. To solve this problem, we address non-orthogonal multiple access (NOMA) assisted by intelligent reflecting surfaces (IRSs) to accommodate more devices and tailor their channels favorably to the FL performance. For the FL with IRS-NOMA, we minimize the total latency by reducing the latency per training round dominated by local computation and uplink communication through optimization of IRS-NOMA strategies and improving the training efficiency under irregular local data through active device selection. We then propose an auction-based IRS allocation that utilizes the optimized total latency for the valuation of the IRSs when multiple base stations of different operators share their neighboring IRSs. Winner determination (WD) and payment methods are devised with multiple bids on IRS subsets in a way of maximizing social welfare. The results show that the proposed latency minimizing algorithm outperforms the benchmarks by improving both communication and training efficiency through device selection combined with IRS-NOMA optimization. In addition, the auction mechanism with the proposed WD outperforms the benchmarks, where the social welfare is improved by constructing each bid with the valuation on multiple IRSs and increasing the number of bids submitted.

In this paper, we consider a Cloud Radio Access Network (CRAN) based system consisting of one Network Operator (NO) and several Content Providers (CPs). The NO owns a cloud cache and provides caching as a service for CPs, who provide contents to users. While the NO wishes to motivate CPs to rent its cache and maximize its profit, CPs want to optimize the service performance for users and their renting utilities. Due to the time separation between cache allocation and user association problems, we model the interactions between the NO and CPs as a hierarchical game, i.e., a cache renting scheme between the NO and CPs in the cache allocation problem and the willingness of CPs in the user association problem. In the cache allocation problem, we propose a contract theory-based incentive mechanism in which the NO designs and offers an optimal contract to various types of CPs. We then formulate the user association problem as a many-to-many matching game with externalities. To solve this matching game, we propose a matching algorithm that converges to a two-sided exchange stable matching with low complexity. The simulation results demonstrate that this proposed approach is beneficial to the NO’s profit and incentivize the CP to rent the cache with truthful private information. In addition, the system performance of the proposed approach in term of the total datarate-delay tradeoff outperforms than the benchmarks.