This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated Intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter.

The well log is the basis for understanding a geologic structure and evaluating petroleum reservoirs. It inevitably leads to missing logs due to borehole conditions and equipment failures. Existing machine-learning methods introduce convolutional neural networks (CNNs) in temporal networks to learn local morphological (spatial) features for improving prediction. However, they ignore the temporal background information of the logs and the differences in spatial features at different depth points. An adaptive spatiotemporal transformer (ASTT) is developed to effectively overcome these challenges, which consists of a temporal background encoder (TBE), a spatial encoder (SE), and a spatiotemporal decoder (STD). TBE introduces average pooling in the transformer encoder to learn the hidden geologic information in the extracted longitudinal temporal features. SE combines CNN and an attention mechanism to learn the spatial features of each depth point differentially. STD maps the extracted spatiotemporal features to the missing logs. Experimental results on real oilfield data indicate that our ASTT achieves excellent performance in terms of fitting degree and test error. The results in the cross-logs and crosswell cases demonstrate the generalization of ASTT.

Based on an investigation of online public opinion on the Nahel Merzouk protests in France, an approach for analysing and predicting public opinion on protests based on large language model (LLM) is proposed, revealing the impact of emerging social media on the protests. We demonstrate that protests generate public opinion on social media with some lag, but that comment sentiment and expression are consistent with protest trends. As the protests unfolded, we analyzed the evolution of public sentiment. We constructed the prompt based on historical data to predict the protests using the p-tuning and Lora approach to fine-tune LLM. In addition, we discuss how to use blockchain technology to optimize distributed, self-organizing protests and reduce the potential for disinformation and violent conflict.

As distributed sensing technologies evolve, the collection of time series data is becoming increasingly decentralized, which introduces serious challenges for both model training and data privacy protection. In response to this trend, federated time series anomaly detection enables collaborative analysis across distributed sensing nodes without exposing raw data. However, federated anomaly detection experiences issues with unstable training and poor generalization due to client heterogeneity and the limited expressiveness of single-path detection methods. To address these challenges, this study proposes FedSW-TSAD, a federated time series anomaly detection method based on the Sobolev–Wasserstein GAN (SWGAN). It leverages the Sobolev–Wasserstein constraint to stabilize adversarial training and combines discriminative signals from both reconstruction and prediction modules, thereby improving robustness against diverse anomalies. In addition, FedSW-TSAD adopts a differential privacy mechanism with L2-norm-constrained noise injection, ensuring privacy in model updates under the federated setting. The experimental results determined using four real-world sensor datasets demonstrate that FedSW-TSAD outperforms existing methods by an average of 14.37% in the F1-score while also enhancing gradient privacy under the differential privacy mechanism. This highlights the practical value of FedSW-TSAD for privacy-preserving anomaly detection in sensor-based monitoring systems such as industrial IoT, remote diagnostics, and predictive maintenance.

IoT devices have been widely deployed in medical industry, in the objective of improving diagnostic accuracy and increasing the efficiency of healthcare systems. However, traditional centralized learning approaches often fall short in meeting strict privacy requirements and adapting to emerging diseases in clinical environment. To address this, we propose a novel federated continual learning (CL) framework for disease diagnosis (FCL4DD), designed to enable distributed and incremental learning of new disease classes while safeguarding data privacy. To combat catastrophic forgetting in CL, FCL4DD integrates a replay strategy powered by a weakly supervised diffusion model (WSDM) to generate historical data for diagnosis model training. The WSDM leverages weak supervision into diffusion model to capture the diverse characteristics of the real data, enabling the generation of high-quality synthetic samples that maintain the data’s inherent variability. To overcome the challenges of nonindependent and identically distributed (non-IID) data in federated learning, WSDM is deployed at the central server to generate synthetic disease data that conforms to the global distribution. This synthetic data is then used to retrain client models, reducing discrepancies and enhancing performance consistency across clients. Evaluations on various datasets demonstrates that our method outperforms other state-of-the-art approaches, such as FedEWC, FedLwF, FedWeIT, TARGET, and DDDR, achieving up to a 4.85% accuracy improvement over the second-best method. Code are available at https://github.com/hysshy/FCL4DD.