With the widespread adoption of intelligent devices such as Augmented Reality (AR) glasses and Unmanned Aerial Vehicles (UAVs) in power equipment inspection, data privacy and efficient on-device inference becomes increasingly important. Federated Learning (FL) offers a solution by enabling collaborative model training without sharing raw data. However, naive model aggregation in FL often suffers from inefficiencies due to data and device heterogeneity, and the resulting models are typically too large-scale for deployment on resource-constrained terminals. To address these challenges, we propose FedCSAD, a hierarchical FL framework that combines Contextual Bandit-based Client pair Selection (CBCS) and Confidence-weighted Multi-teacher Knowledge Distillation (CMKD). CBCS optimizes aggregation by adaptively selecting high-quality model pairs using contextual information, while CMKD compresses global and edge models into compact student models suitable for terminal deployment. Experiments on CIFAR-10 and InsPLAD, a dataset based on real industrial data, demonstrate that FedCSAD outperforms existing methods in model accuracy, convergence speed, and deployment efficiency, offering a scalable and privacy-preserving solution for real-time intelligent inspection in the power industry.
With the rapid development of Large Language Models (LLMs), Embodied Artificial Intelligence (EAI) has entered a new phase of advancement. However, in real-world human–robot interactions, the ambiguity of user instructions continues to pose significant challenges to the feasibility and safety of task execution. Existing approaches have notable limitations: rule-based static safety constraints struggle to adapt to diverse and dynamic scenarios; multimodal models often lack fine-grained risk identification; and reinforcement learning-based approaches rely heavily on trial-and-error, resulting in delayed responses to potential risks. To address these challenges, we propose RoboClarify, a scenario-guided instruction clarification approach. Based on objects in the environment, this approach evaluates both the inherent basic risk and the distance-based risk between objects. It then integrates a constructed Scenario Risk Graph with the semantic reasoning capabilities of LLMs to identify potential risk relations and guide the transformation of ambiguous instructions into semantically clear and executable ones. This process enhances task understanding and execution in complex environments. We conducted empirical evaluations using the public SafeAgentBench dataset and our newly constructed HomeAgent dataset. The results show that three mainstream LLMs (GPT-4o mini, DeepSeek, and Qwen) achieved clarification rates exceeding 94
As quantum computing poses a significant threat to traditional cryptographic systems, the need for post-quantum cryptographic (PQC) solutions has become urgent. Implementing these lattice-based algorithms on resource-constrained platforms, such as embedded systems and IoT devices, presents challenges. These challenges stem from the high computational and memory demands of the algorithms. Existing hardware designs often focus on accelerating specific computational tasks, like the Number Theoretic Transform (NTT), but are usually tailored to individual algorithms, making them inefficient for multi-algorithm systems. In this work, we address these challenges by designing KiRa: A Unified Memory Architecture for PQC that supports both CRYSTALS-Dilithium and CRYSTALS-Kyber at all NIST security levels. The architecture allows core components, such as Keccak, NTT, and sampling modules, to be shared across both Dilithium and Kyber algorithms. A uniform control logic and dataflow enable seamless switching between the two algorithms without duplicating resources. To support high-throughput polynomial operations, KiRa includes a dual-port memory design that efficiently handles polynomial data, and features a high-throughput NTT engine compatible with both algorithms. We implement and test the design on a Zynq UltraScale+ MPSoC FPGA platform. The results demonstrate a significant reduction in logic usage, memory access frequency, and dynamic power consumption, while improving latency for signing and decryption operations. Our results show that KiRa achieves a 23.8
Security vulnerabilities in smart contracts are a primary risk for Decentralized Finance (DeFi) and blockchain systems. Current research focuses on detection, neglecting end-to-end automated management. To address this, we propose LLM-BSCVM, an LLM-based framework for automated vulnerability management. We introduce a “Decompose–Retrieve–Generate” methodology that breaks down the workflow into six sub-tasks for specialized agents: detection, repair suggestion, risk assessment, repair, patch verification, and report generation. These agents collaborate in an automated chain, retrieving information from knowledge bases to enhance reasoning. Our evaluation shows LLM-BSCVM achieves an accuracy and F1-score over 91 https://github.com/sosol717/LLM-BSCVM .
With an increasing volume of data being collected and processed on edge, Federated Learning (FL) has become a dominant approach to train shared global models on distributed data held by diverse edge clients without exposing privacy. However, with expectations of high-quality user experience, instantaneous intelligent decisions must be made at the network edge to meet performance, security, and latency requirements. Intuitively, this challenge becomes more daunting as edge devices are constrained by limited computing and storage resources. Furthermore, edge clients need to continuously acquire, update, accumulate, and apply knowledge in domains requiring the processing of sensitive data and long-term model training. It has been proven that edge clients often forget previous tasks knowledge after learning new tasks, resulting in catastrophic forgetting. To mitigate this, we propose a Federated Meta Continual Learning (FMCL) approach. It requires edge clients not only to learn new tasks quickly but also to memorize rapidly. Specifically, we adopted a MAML (Model-Agnostic Meta-Learning) framework with memory replay, setting up buffers at edge clients to consolidate old tasks. In such a way, the model learns optimal initialization to quickly adapt to all seen tasks while preventing catastrophic forgetting. Extensive evaluation results indicate that FMCL performs well in alleviating catastrophic forgetting and achieves a good balance between learning new tasks and remembering existing tasks.
