Personalized Federated Learning (PFL) tackles the challenges of FL on heterogeneous data and provides customized solutions to each client. However, like commonly employed FL settings, PFL is still vulnerable to attacks on privacy and model availability. Existing PFL frameworks focus on either privacy protection or attack defense instead of simultaneously implementing both functionalities. To address this challenge, we design and implement a novel Privacy-preserving and Robust Personalized Federated Learning framework, PR-PFL, which can simultaneously protect data privacy and defend against model availability attacks. Specifically, PR-PFL adopts Mean Regularized Multi-Task Learning (MR-MTL) as the base training paradigm. Clients perform per-sample DP to protect data privacy, and the central server executes a robust aggregation algorithm to filter out potential attackers. After collective training, clients tune their models locally to eliminate malicious injections further. To the best of our knowledge, this is the first PFL framework that protects both clients’ privacy and model availability. The method combining robust aggregation and local tuning we have designed can effectively defend against 5 kinds of attacks. We conduct an extensive empirical evaluation demonstrating that our framework is practical and achieves reasonable robustness under an honest majority setting (attackers <50%).
In the Multi-sensor multi-vehicle (MSMV) cooperative localization system, the data incest problem generates inter-estimate correlation, leading to over-convergence problems. For the purpose of solving the data incest problem, we propose a method named interval split covariance intersection filter (ISCIF). First, the consistency proof of ISCIF is provided. Then, ISCIF is applied to achieve vehicle cooperative localization, including both absolute and relative positioning stage. Moreover, in order to reduce the computational overhead and address pessimistic estimation problems caused by multiple relative position estimates in the MSMV cooperative localization system, the interval constraint propagation (ICP) method is employed for data preprocessing before relative positioning using ISCIF. Finally, two different simulation experiment scenarios are designed to validate localization accuracy. Simulation results demonstrate that our proposed method, compared to the traditional split covariance intersection filter (SCIF) method, reduces the root mean square error (RMSE) by 13% and 21% in two different experimental scenarios, respectively.
Due to the pressing demand for advanced 6G and beyond communications technology that can effectively handle large-scale requirements with utmost security and efficiency. With the growth and spread of companies and institutions that deal with a large amount of data, protecting data and ensuring privacy has become a basic requirement. Actual reality and recent research have shown a significant increase in phishing scams. This research seeks to develop the security level of Li-Fi technology by detecting and preventing phishing activities. It is adopted to inspect URL links transmitted over the network before they reach the end user. The proposed system was tested using two-layer algorithms to detect and prevent hacking and phishing. The performance of the proposed system was examined on Li-Fi technology via FSO under different weather conditions. The proposed system has demonstrated ambitious results in implementing tasks successfully and effectively.
PointNet was a breakthrough in neural networks for directly processing point cloud data, ensuring transformation and input invariance with a simple, efficient structure. However, it neglects the relationships between individual points’ features. This paper introduces an improved model, KMeans-PointNet, which combines PointNet with K-Means clustering. By clustering point cloud data and extracting global features from each cluster, the model enhances local feature utilization. Experiments on the ModelNet40 dataset show that KMeans-PointNet improves overall recognition accuracy by 3% and classification accuracy by 2% compared to the original PointNet.
Various Internet of Things (IoT) devices bring great convenience to users. However, the privacy protection and overall performance still need improvements due to their limited storage and computation abilities. Cloud computing can be used to solve or lessen these issues by providing server-level security, unlimited storage, and high computation abilities. It is time-consuming to conduct all the calculation in local IoT devices. It is also not wise to offload everything to Cloud, because the data could be massive that takes longer time to transfer than to compute. In this paper, we design a framework in which the local IoT devices are the cache and the remote Cloud servers are the memory. To further improve system efficiency and adaptability, we use some lightweight Artificial Intelligence (AI) methods to predict users’ behavior patterns, that can adapt to changes while maintaining optimal performance.
