Federated Unlearning (FUL) focuses on client data and computing power to offer a privacy-preserving solution. However, high computational demands, complex incentive mechanisms, and disparities in client-side computing power often lead to long times and higher costs. To address these challenges, many existing methods rely on server-side knowledge distillation that solely removes the updates of the target client, overlooking the privacy embedded in the contributions of other clients, which can lead to privacy leakage. In this work, we introduce DPUL, a novel server-side unlearning method that deeply unlearns all influential weights to prevent privacy pitfalls. Our approach comprises three components: (i) identifying high-weight parameters by filtering client update magnitudes, and rolling them back to ensure deep removal. (ii) leveraging the variational autoencoder (VAE) to reconstruct and eliminate low-weight parameters. (iii) utilizing a projection-based technique to recover the model. Experimental results on four datasets demonstrate that DPUL surpasses state-of-the-art baselines, providing a 1%-5% improvement in accuracy and up to 12x reduction in time cost.

Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing has been proposed as an efficient task-offloading solution for user equipments (UEs). Nevertheless, the presence of heterogeneous UAVs makes centralized navigation policies impractical. Decentralized navigation policies also face significant challenges in knowledge sharing among heterogeneous UAVs. To address this, we present the soft hierarchical deep reinforcement learning network (SHDRLN) and dual-end federated reinforcement learning (DFRL) as a decentralized navigation policy solution. It enhances overall task-offloading energy efficiency for UAVs while facilitating knowledge sharing. Specifically, SHDRLN, a hierarchical DRL network based on maximum entropy learning, reduces policy differences among UAVs by abstracting atomic actions into generic skills. Simultaneously, it maximizes the average efficiency of all UAVs, optimizing coverage for UEs and minimizing task-offloading waiting time. DFRL, a federated learning (FL) algorithm, aggregates policy knowledge at the cloud server and filters it at the UAV end, enabling adaptive learning of navigation policy knowledge suitable for the UAV's performance parameters. Extensive simulations demonstrate that the proposed solution not only outperforms other baseline algorithms in overall energy efficiency but also achieves more stable navigation policy learning under different levels of heterogeneity of different UAV performance parameters.

Mobile crowdsensing leverages the power of a vast group of participants to collect sensory data, thus presenting an economical solution for data collection. However, due to the variability among participants, the quality of sensory data varies significantly, making it crucial to extract truthful information from sensory data of differing quality. Additionally, given the fixed time and monetary costs for the participants, they typically only perform a subset of tasks. As a result, the datasets collected in real-world scenarios are usually sparse. Current truth discovery methods struggle to adapt to datasets with varying sparsity, especially when dealing with sparse datasets. In this article, we propose an adaptive Hypergraph-based EM truth discovery method, HGEM. The HGEM algorithm leverages the topological characteristics of hypergraphs to model sparse datasets, thereby improving its performance in evaluating the reliability of participants and the true value of the event to be observed. Experiments based on simulated and real-world scenarios demonstrate that HGEM consistently achieves higher predictive accuracy.

Anomaly detection aims to identify data or behav-iors that are different from the usual patterns. In traditional anomaly detection settings, edge devices collect the data and send it to a centralized server for model training, which faces two critical issues: (1) it risks data exposure during transmission; (2) it demands a large amount of network bandwidth for data transfer. To tackle these problems, we propose a Visual Federated Learning algorithm (VFLA) for anomalous behavior identification in the multi-UAVs system. To the best of our knowledge, we are the first to merge federated learning with video-based anomaly detection. VFLA consists of two phases: The initial phase is training a pseudo-label generator. UAVs collect a dataset and manually annotate it. This labeled data is then used to train the pseudo-label generator on the server, which is subsequently distributed back to the UAVs. The second phase is the federated learning-based anomaly detection model training. UAVs leverage the pseudo-label generator to automatically annotate the collected video footage. These annotated videos are fed into an anomaly detection network for training. Once the local training is completed, UAVs upload their local models to a server for federated aggregation. The global model is then redistributed to the UAVs for additional training rounds, until reach the target accuracy. Finally, we simulate the federated learning anomaly detection algorithm on the Shanghai-tech dataset, it demonstrates an average accuracy boost of 5.6% compared to baselines.

The problem of resource allocation in a high mobility network is always meaningful while challenging. Due to the mobility characteristic, the main difficulty lies in solving different optimization problems in a limited time, so traditional time-consuming optimization solvers are no longer applicable. This paper considers NGMA-enabled heterogeneous networks (HetNets) and uses end-to-end multi-agent reinforcement learning (MARL) to optimize the resource allocation problem. Even though MARL can use many cooperative agents to divide action space, it may lead to a significant increase in agent number, which is harmful to credit assignment. To tackle this problem, we employ a unique design that can fix the number of agents in any case. Agent credit assignment is then considered to guide agents to work cooperatively and ensure each efficient action gets a suitable reward. Also, a novel learning process named Learn to Improve is utilized to make our method more general. Numerical results and comparison experiments show the effectiveness and robustness of our methods.

With increasing more powerful sensing capacities of mobile devices, the Mobile Crowd Sensing (MCS) system requires to collect larger sensing data from participants. Nevertheless, collecting such large volume of data will cost a lot for participants, base stations and MCS server. Even worse, some sensing data cannot satisfy the MCS sensing requirement due to the low quality and are filtered by the MCS server in clouds. Inspired by the D2D technique, where mobile devices can communicate directly with the help of the nearby base station, in 5G networks, we propose the Reliable Data Collection (RDC) algorithm to validate the generated sensing data at device sides in this paper. To be specific, the whole progress is formulated as a Probability problem of Discovering Reliable sensing data (PDR) at client sides, and Expectation Maximization (EM) is leveraged to devise the algorithm. Finally, the extensive simulations and real-world use case are conducted to evaluate the performance of RDC algorithm, and the result shows that RDC outperforms the other two benchmarks in estimating accuracy and saving data collection cost.

Geomagnetic field signal has gained increasing wide investigated for indoor positioning problems. Because of the variation of magnetic signals and the sensor observation drift, the recent positioning technology development based on magnetic field or pedestrian dead reckoning (PDR) has been restricted. In addition, the accumulative error and cold-start problem can also cause huge positioning error. In this paper, we present a novel indoor localization approach, GeoLoc, for combining magnetic fingerprint matching and PDR by Kalman Filter. First, magnetic field intensity of every positions is gathered and a fingerprint map is built for matching. A candidate set of positions is introduced to include uncertainty and increase robustness for our estimation. With the squeezing of candidate sets, uncertainties of orientation and position estimation have been eliminated. Realistic experiment results show that GeoLoc successfully addresses accumulative error and cold-start problems by sensor data fusion. GeoLoc achieves a good estimation for both short (less than 17.5m) and long walking distance, and it can work in both offline and online real-time positioning. GeoLoc is able to achieve an online positioning accuracy of less than 1.2m, and an offline positioning accuracy of 0.3m only with a smart phone. GeoLoc only uses the built-in sensors of mobile phones, thus users can get their position only by using their phone.