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.

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.