With the advancement of vehicle-to-vehicle (V2V) ad hoc networks and wireless communication technologies, mobile edge caching has become a key enabler for enhancing network performance and user experience. However, traditional federated learning-based collaborative caching approaches in vehicular scenarios suffer from inadequate client selection mechanisms and limited prediction accuracy, which result in suboptimal cache hit ratios and increased content transmission latency. To address these challenges, we propose a Digital Twin-based Asynchronous Federated Learning-driven Predictive Edge Caching with Deep Reinforcement Learning (DAPR) framework. DAPR employs an intelligent client selection strategy based on asynchronous federated learning, which leverages mobility prediction and data quality assessment to avoid selecting highly mobile clients or clients with low-quality data, thereby significantly improving model convergence efficiency. In addition, we design a GRU-VAE prediction model that uses a Variational Autoencoder (VAE) to capture latent data distribution features and Gated Recurrent Units (GRUs) to model temporal dependencies, thereby substantially enhancing the accuracy of content request prediction. The predicted content popularities are then fed into a deep reinforcement learning-driven caching decision engine to dynamically optimize edge caching resource allocation. Extensive experiments demonstrate that DAPR achieves superior performance in terms of average reward, cache hit ratio, and transmission latency, thereby effectively improving the overall efficiency of vehicular edge caching systems.

Federated Learning (FL) is a distributed machine learning method that allows multiple devices to train models locally and aggregate updates at a central server to ensure data privacy. However, constructing a robust global model is challenging due to limited access to local data and heterogeneous client data. Furthermore, malicious clients and high communication and computation costs further complicate the design of FL algorithms. Existing methods for evaluating client contributions are computationally intensive, unsuitable for large-scale applications. To overcome these challenges, this paper proposes an adaptive weighting FL framework based on the Banzhaf index, providing a more efficient mechanism for evaluating and allocating client contributions. The Banzhaf index simplifies the computation process compared to traditional methods, allowing for better scalability. Additionally, we introduce a Neyman allocation stratified sampling strategy to optimize client selection, which effectively reduces variance and ensures balanced client representation, further enhancing both model performance and computational efficiency. Experimental results show that the proposed method significantly improves training efficiency, model accuracy, and robustness across various datasets, while reducing computation costs, making it well-suited for large-scale FL.

Personalized federated learning has gained significant attention due to its effectiveness in addressing data heterogeneity and achieving personalization. To train a model that performs well on local datasets, the key is to enable more effective knowledge sharing between clients and servers without compromising data privacy. Most current approaches aggregate partial model on central server for collaborative learning. However, the information conveyed through model parameters is limited. In this work, we propose a dual-information guided personalized federated learning approach called FedDCI. To obtain more collaborative information, we capture information from both model Parameters and data features. By implementing prototypes similarity-based aggregation, we generate client-specific global models and prototypes, effectively mitigating the challenges posed by data heterogeneity in Federated Learning. We conduct extensive experiments in both label skew and feature skew settings using four benchmark datasets. Experimental results demonstrate that FedDCI achieve accuracy improvements over existing methods in complex heterogeneous scenarios.

Federated learning (FL) enables distributed clients to collaborate on training while storing their data locally to protect client privacy. However, due to data heterogeneity, including issues related to label distributions skew in heterogeneous scenarios, the resulting global model may not be suitable for all clients. In this work, we introduce a personalized federated learning method called pFedPM, which focuses on addressing this challenge of label distributions skew in heterogeneous scenarios. We replace traditional gradient uploading with feature uploading, and introduce a novel feature fusion scheme to learn personalized local model for clients. Specifically, the server receives feature information from clients, aggregates global features, and sends them back to the clients. Clients achieve personalization by fusing local and global features. Furthermore, we introduce a relation network as an additional decision layer, providing a non-linear learnable classifier to predict labels. Through the novel modeling techniques, our proposed method reduces communication costs and supports heterogeneous client models. Experimental results demonstrate that our approach outperforms recent FL methods on the MNIST, FEMNIST, and CIFAR-10 datasets while requiring less communication.

Federated learning (FL) is a scheme that enables multiple participants to cooperate to train a high-performance machine learning model in a way that data cannot be exported. FL effectively protects the data privacy of all participants and reduces communication costs. However, a key challenge for federated learning is the data heterogeneity across clients. In addition, in real FL applications, the class distribution of data is usually unbalanced. Although many researches have been conducted to solve the problem of data heterogeneity, class imbalance problem usually arises along with the heterogeneity data, resulting in the poor performance of the global model. In this paper, a novel FL method (we call it FedEF) is designed for heterogeneous data and local class imbalance problem via optimize feature extractors and classifiers. FedEF optimizes the local feature extractor representation of individual clients through contrastive learning to maximize the consistency of the feature extractor representation trained by the local client and the central server to handle heterogeneous data. Meanwhile, we modified the cross entropy loss in the model, assigned different loss weights to different classes of data, paid more attention to the class with fewer samples in the training process, and corrected the biased classifier to alleviate the problem of class imbalance, thus can improve the performance of the global model. Experiments show that FedEF is an effective solution to FL model obtained under heterogeneous and local class imbalance.

Symmetric Searchable Encryption(SSE) is deemed to tackle the privacy issue as well as the operability and confidentiality in data outsourcing. However, most SSE schemes assume that the cloud is honest but curious. This assumption is not always applicable. In this paper, we propose an efficient SSE scheme based on B+-Tree and Counting Bloom Filter (CBF) which supports secure verification, dynamic updating, and multi-user queries. Comparing with the previous state of the arts, we design the new data structure CBF to support dynamic updating and boost verification. we evaluate our scheme through comprehensive experiments. The results are consistent with our analysis and show that our scheme is secure, and more efficient compared with the previous schemes with the same functionalities.The average performance can be improved by about 20% for both the cloud servers and users when the missing rate of the searching keywords is 20%. And the higher the missing rate is, the more the performance can be improved.

In this paper, we propose a scheme that supports statistic query and authorized access control on an Encrypted Electronic Medical Records Databases(EMDB). Different from other schemes, it is based on Differential-Privacy(DP), which can protect the privacy of patients. By deploying an improved Multi-Authority Attribute-Based Encryption(MA-ABE) scheme, all authorities can distribute their search capability to clients under different authorities without additional negotiations. To our best knowledge, there are few studies on statistical queries on encrypted data. In this work, we consider that support differentially-private statistical queries. To improve search efficiency, we leverage the Bloom Filter(BF) to judge whether the keywords queried by users exists. Finally, we use experiments to verify and evaluate the feasibility of our proposed scheme.

With the prosperity of mobile application markets, mobile advertising is becoming an increasingly important economic force. In order to maximize revenue, ads are recommended to be delivered to potentially interested users, which requires user targeting, i.e., analyzing users' profiles and exploring users' interests. However, collecting user personal information for targeted mobile advertising services raises critical privacy concerns. Although some solutions like anonymization and obfuscation have been proposed for privacy-aware targeted advertising, they undesirably face the issues of security, efficiency, and/or ad relevance. In this paper, we propose a practical system enabling secure and efficient targeted mobile advertising services. It allows the ad network to perform accurate user targeting, while ensuring strong privacy protection for mobile users. Specifically, we show how to properly leverage a cryptographic primitive called private stream searching to support secure, accurate, and practical targeted mobile ad delivery. Moreover, we propose secure billing schemes to enable the ad network to charge advertisers in a privacy-preserving manner. The security strength of our system is thoroughly analyzed. Through extensive experiments, we show that our system achieves practical efficiency on mobile devices.

Mobile advertising has become a key economic force with the growing mobile app markets. Targeted advertising has been attracting the attention of business and research communities because of its high return on investment. However, the fact that it requires continuous collection of user profiles raises severe privacy concerns. In this paper, we propose a secure and practical mobile advertising architecture, which aims to enable targeted advertising over encrypted mobile user profiles. The proposed idea is based upon a cryptographic primitive, i.e., private stream searching (PSS). We first propose a basic construction of secure targeted mobile advertising. Thorough analysis shows that our basic design achieves semantic security. After that, we introduce a number of techniques to make our design more efficient for mobile devices. Experimental results demonstrate the practicality of our proposed architecture.