Large Language Models (LLMs) increasingly underpin intelligent web applications, from chatbots to search and recommendation, where efficient specialization is essential. Low-Rank Adaptation (LoRA) enables such adaptation with minimal overhead, while federated LoRA allows web service providers to fine-tune shared models without data sharing. However, in privacy-sensitive deployments, clients inject varying levels of differential privacy (DP) noise, creating privacy heterogeneity that misaligns individual incentives and global performance. In this paper, we propose WinFLoRA, a privacy-heterogeneous federated LoRA that utilizes aggregation weights as incentives with noise awareness. Specifically, the noises from clients are estimated based on the uploaded LoRA adapters. A larger weight indicates greater influence on the global model and better downstream task performance, rewarding lower-noise contributions. By up-weighting low-noise updates, WinFLoRA improves global accuracy while accommodating clients' heterogeneous privacy requirements. Consequently, WinFLoRA aligns heterogeneous client utility in terms of privacy and downstream performance with global model objectives without third-party involvement. Extensive evaluations demonstrate that across multiple LLMs and datasets, WinFLoRA achieves up to 52.58% higher global accuracy and up to 2.56× client utility than state-of-the-art benchmarks. Source code is publicly available at https://github.com/koums24/WinFLoRA.git.

The burgeoning 5G network brings edge servers closer to users to host online applications. These edge servers are typically kept running 24/7 to meet users' computational demands. However, the user coverage, privacy assurance, and service delay have consistently undermined users' confidence, compounded by the significant environmental damage caused by excessive energy consumption. Recently, various approaches have been proposed to tackle the energy-saving demand response issue in the multi-access edge computing (MEC) system. Unfortunately, existing attempts often compromise service quality and energy efficiency for privacy enhancement, and incur significant computational overheads and delays unsuitable for real-time services. Therefore, maintaining satisfying user coverage with energy consumption while adhering to users' privacy demands with low computational overhead is critical to achieving sustainable edge services. To address those challenges, we systematically formulate the location-privacy-preserving edge demand response (LEDR) problem and introduce a novel system named KGEES. KGEES incorporates$k$-anonymity geo-obfuscation to enhance user privacy while leveraging a heuristic approach to finalize resource allocation strategies under geo-distortion greedily to jointly improve system utility, energy, and time efficiency. Comprehensive experiments on a real-world dataset demonstrate that KGEES surpasses the representative approaches by an average of$1.187 \times$in system utility and$1.192 \times$in energy efficiency while being$ 203.5 \times$faster.

Caching web data on edge servers has become a common practice in latency-sensitive services to minimize data retrieval delays for web users. However, the geographic distribution of edge servers and frequent data transmissions make these systems vulnerable to security threats, particularly cache pollution attacks (CPAs). In such attacks, malicious users send excessive requests for unpopular data at abnormal frequencies, causing irrelevant content to be cached and degrading the system’s performance. Traditional CPAs, though impactful in conventional caching systems, are less effective in edge environments where user requests are more diverse and edge servers collaborate in caching strategies. In this paper, we identify a novel attack named data flipping attack (DFA) that targets the data transmission process among edge servers. This attack manipulates request distribution by swapping the frequencies of popular and unpopular data requests, all while maintaining other characteristics like request timing and user identity. This tactic disrupts caching strategies without raising suspicion. Experimental results indicate DFA is independent of user request patterns and demonstrates substantial effectiveness and robustness, successfully forcing edge web users to retrieve data from the cloud across various scales and configurations of edge networks. Furthermore, it evades detection by state-of-the-art methods that rely on specific distribution patterns, such as the Zipf distribution. To counter this attack, we propose an effective defense method that alters the request distribution by frequency distillation, mitigating its impact.

As the 5G network rapidly expands, more edge servers are being deployed to provide more efficient and low-latency mobile services. However, limited edge resources constrain users' demand response, while continuous server operation leads to significant energy consumption, undermining the sustainability of the multi-access edge computing (MEC) system. Existing resource allocation methods rely on accurate user locations, which can lead to privacy exposure, while protection techniques often result in significant service degradation due to spatial distortion. Moreover, user mobility in MEC systems poses new challenges for edge resource management, which requires dynamic server collaboration and user data migration, incurring additional costs and delays. To address these challenges, we propose MoSEEC, which employs user-adaptive differential geo-obfuscation to secure trajectory privacy while dynamically enhancing service performance with energy awareness. Our results demonstrate its superior performance in migration delays and system utility by$1.54 \times$faster and$1.15 \times$higher compared to existing techniques with privacy guarantees, respectively. In addition, our system outperforms state-of-the-art approaches by$5 \times$faster on average in terms of computation overhead.

Multi-access edge computing (MEC) brings computational resources closer to end-users by widely distributing the physical edge services, reducing end-to-end service latency at the network edge. Continuous operation of edge servers results in high energy usage and significant carbon footprints. Efficient resource management is essential for MEC sustainability. Recent solutions focus on demand response to reduce energy consumption. However, the reduction in available resources due to server shutdowns forces a degradation in the quality of service (QoS) provided to users, significantly impacting their quality of experience (QoE). Moreover, the non-linear relationship between QoS and QoE further complicates the issue. Therefore, maintaining user's QoE with energy consumption is critical to achieving sustainable edge services. To tackle these challenges, we formulate the QoE-aware energy saving (QoEES) problem and propose QESGame, a game-theoretical algorithm to solve this problem effectively and efficiently with a guaranteed convergence to Nash equilibrium. Through extensive evaluations, we demonstrate that QESGame surpasses the representative approaches by up to 20.21%, 41.62%, and 23.54% in terms of the overall system benefit, energy saving, and QoE.

The right to be forgotten mandates that machine learning models enable the erasure of a data owner's data and information from a trained model. Removing data from the dataset alone is inadequate, as machine learning models can memorize information from the training data, increasing the potential privacy risk to users. To address this, multiple machine unlearning techniques have been developed and deployed. Among them, approximate unlearning is a popular solution, but recent studies report that its unlearning effectiveness is not fully guaranteed. Another approach, exact unlearning, tackles this issue by discarding the data and retraining the model from scratch, but at the cost of considerable computational and memory resources. However, not all devices have the capability to perform such retraining. In numerous machine learning applications, such as edge devices, Internet-of-Things (IoT), mobile devices, and satellites, resources are constrained, posing challenges for deploying existing exact unlearning methods. In this study, we propose a Constraint-aware Adaptive Exact Unlearning System at the network Edge (CAUSE), an approach to enabling exact unlearning on resource-constrained devices. Aiming to minimize the retrain overhead by storing sub-models on the resource-constrained device, CAUSE inno-vatively applies a Fibonacci-based replacement strategy and updates the number of shards adaptively in the user-based data partition process. To further improve the effectiveness of memory usage, CAUSE leverages the advantage of model pruning to save memory via compression with minimal accuracy sacrifice. The experimental results demonstrate that CAUSE significantly outperforms other representative systems in realizing exact unlearning on the resource-constrained device by 9.23%-80.86%, 66.21%-83.46%, and 5.26%-194.13% in terms of unlearning speed, energy consumption, and accuracy.

The global deployment of the 5G network has led to a substantial increase in the deployment of edge servers to host web applications, catering to the growing demand for low service latency by edge web users. Yet, running edge servers 24/7 leads to enormous energy consumption and excessive carbon emissions. Energy-efficient edge resource provision is desired to achieve sustainable development goals in the new multi-access edge computing (MEC) architecture. Recently, several approaches have been proposed to solve the demand response problem for energy saving in cloud computing and MEC. However, accurate location information of edge web users should always be provided, which sacrifices users' privacy. To protect edge web users' location privacy while saving energy in MEC, we systematically formulate this location privacy-preserving edge demand response (LEDR) problem. To solve the LEDR problem effectively and efficiently, we propose a system named GEES by incorporating differential geo-obfuscation to secure user privacy while maximizing system utility and energy efficiency through inferences with theoretical analysis. Extensive and comprehensive experiments are conducted based on a synthetic real-world dataset, and the results demonstrate that GEES outperforms representative approaches by 23.02%, 31.47%, and 17.29% on average in terms of energy efficiency, user privacy and system utility.