Federated learning (FL) enables collaborative model training across distributed nodes without exposing raw data, but its decentralized nature makes it vulnerable in trust-deficient environments. Inference attacks may recover sensitive information from gradient updates, while poisoning attacks can degrade model performance or induce malicious behaviors. Existing defenses often suffer from high communication and computation costs, or limited detection precision. To address these issues, we propose LSHFed, a robust and communication-efficient FL framework that simultaneously enhances aggregation robustness and privacy preservation. At its core, LSHFed incorporates LSHGM, a novel gradient verification mechanism that projects high-dimensional gradients into compact binary representations via multi-hyperplane locality-sensitive hashing. This enables accurate detection and filtering of malicious gradients using only their irreversible hash forms, thus mitigating privacy leakage risks and substantially reducing transmission overhead. Extensive experiments demonstrate that LSHFed maintains high model performance even when up to 50% of participants are collusive adversaries, while achieving up to a 1000× reduction in gradient verification communication compared to full-gradient methods.

Web service-oriented transactions have become an integral part of the Internet economy, with the mainstream transaction patterns relying primarily on cloud service markets. However, traditional service transaction methods have deficiencies in terms of both trust and scalability. Distrust between service provider (SP) and consumer (SC), particularly around online payments and data security, impedes the further growth of service transactions. Although blockchain-based transaction mechanisms have made notable progress in addressing trust issues, they still face performance bottlenecks. To tackle these challenges, this paper introduces TrustPay, a service transaction framework that leverages a dual-layer blockchain structure consisting of a parent chain and multiple subchains. The framework partitions the subchain network based on business, with each subchain dedicated to storing service invocation records generated within a specific business unit. The smart contract deployed on the parent chain will settle the invocation records in all subchain networks as transaction records and facilitate automatic transfers among blockchain accounts. This design leverages blockchain's inherent reliability while improving its scalability in large-scale scenarios. Additionally, a novel consensus protocol, REFEREE, is introduced and applied to the subchain network, ensuring efficient recording of invocation data and trusted verification among participants, further enhancing both trust and performance. Comparative experiments and analysis show that TrustPay's dual-layer blockchain structure and REFEREE protocol are not only reliable but also outperform baseline methods in terms of efficiency.

Large Language Models (LLMs) have demonstrated powerful capabilities, yet LLMs face issues like hallucination in certain domain-specific areas. Consequently, an increasing number of domain-specific models are emerging. The current paradigm for domain-specific models involves training with domain data, followed by the employment of Retrieval-Augmented Generation (RAG) to mitigate hallucination issues. However, in precision-critical domains such as manufacturing, if the knowledge documents are of low quality or contain noise, the context retrieved through simple semantic matching by RAG may not necessarily benefit model output. Additionally, there can be issues like getting "lost in the middle" due to irrelevant or excessive context. To overcome this, we introduce the Integrated Term Enhancement Methodology (ITEM). Inspired by Chinese educational methods focused on key term elucidation, ITEM extracts and explains critical terms precisely from knowledge documents to form a comprehensive Term Dictionary for retrieving terms and explanations to enhance query capabilities. This methodology refines query responses by providing more accurate and contextually relevant information. To assess ITEM's effectiveness, we utilize the Chinese Mould Manufacturing Dataset (CMMD) and Contextualized Adaptive Response Assessment (CARA) metric method. Our experiment demonstrates that ITEM significantly outperforms existing retrieval enhancement Dense Retrievers by over 17.0% in accuracy while requiring only 80% of their token length. Moreover, the accuracy of our method exceeded that of GPT-4 by 5.0%. This advancement represents a significant leap in context-specific retrieval in LLMs, especially beneficial for specialized domains. The results underscore ITEM's potential as a transformative method in the field, offering new perspectives on integrating domain-specific knowledge into LLMs.