Federated learning (FL) enables collaborative model training on decentralized Internet of Things (IoT) data while keeping raw data local, thereby mitigating privacy risks. In practice, however, IoT deployments suffer from two coupled challenges: strict uplink bandwidth constraints and long-tailed label distributions where rare events are most critical. Existing work treats these issues separately—communication-efficient FL often neglects data imbalance, whereas federated long-tail methods typically incur heavy communication overheads and privacy risks. We propose FELT, short forFederated Ensemble Learning for Long-Tailed IoT Data, a framework that jointly addresses communication efficiency, long-tail robustness, and privacy through a communication-efficient private voting protocol. Instead of transmitting full model updates, FELT devices function as teacher models, transmitting only single-integer votes on public queries. This architecture reduces communication traffic by up to one order of magnitude (approx. 10×) compared to standard FL methods. On the server, FELT aggregates votes with calibrated noise to ensure (ε, δ)-differential privacy, followed by a post-hoc class-prior-based calibration that improves tail-class predictions with negligible extra computational overhead on the server side. Experiments on multiple real-world IoT datasets demonstrate that FELT substantially boosts tail-class F1 scores (up to 26% improvement) under the same privacy budget. These results highlight FELT as a practical solution for communication-constrained and privacy-sensitive IoT applications.

Smart contracts play a pivotal role in blockchain ecosystems, and fuzzing remains a critical approach to securing them. However, existing smart contract fuzzers often optimize either seed generation or mutation scheduling in isolation and rely on narrow, fragmented feedback signals, leaving multi-transaction reasoning and stagnation recovery under-explored. In this work, we propose aLarge Language Models(LLMs)-based Multi-feedback Smart Contract Fuzzing framework (LLAMA). Key components of the proposed LLAMA include: (i) a hierarchical prompting strategy that guides LLMs to generate structurally valid, context-aware multi-transaction initial seeds, together with a lightweight pre-fuzzing phase that validates and prioritizes high-potential LLM-generated candidates; (ii) a multi-feedback-guided evolutionary optimization module that jointly optimizes seed selection and mutation scheduling by a group of constraints for driving an LLM-bootstrapped bandit scheduler. (iii) an LLM-guided hybrid fuzzing module that integrates evolutionary fuzzing with a dual-channel recovery mechanism, which concurrently employs asynchronous coverage-stagnation- based LLM reseeding and selective symbolic execution to resolve complex path constraints. Our extensive experiments demonstrate that LLAMA outperforms state-of-the-art fuzzers in both coverage and vulnerability detection. Specifically, it achieves 92% instruction coverage on small contracts and 81% on large contracts, while detecting 132 out of 148 known vulnerabilities across diverse categories. Ablation studies further evidence that the proposed multi-feedback and hybrid recovery strategies have strong impact on LLAMA’s performance. The results explain LLAMA’s effectiveness, adaptability, and practicality in complex smart contract scenarios.

In current web environment, fake news spreads rapidly across online social networks, posing serious threats to society. Existing multimodal fake news detection methods can generally be classified into knowledge-based and semantic-based approaches. However, these methods are heavily rely on human expertise and feedback, lacking flexibility. To address this challenge, we propose a Dynamic Analysis and Adaptive Discriminator (DAAD) approach for fake news detection. For knowledge-based methods, we introduce the Monte Carlo Tree Search algorithm to leverage the self-reflective capabilities of large language models (LLMs) for prompt optimization, providing richer, domain-specific details and guidance to the LLMs, while enabling more flexible integration of LLM comment on news content. For semantic-based methods, we define four typical deceit patterns: emotional exaggeration, logical inconsistency, image manipulation, and semantic inconsistency, to reveal the mechanisms behind fake news creation. To detect these patterns, we carefully design four discriminators and expand them in depth and breadth, using the soft-routing mechanism to explore optimal detection models. Experimental results on three real-world datasets demonstrate the superiority of our approach.