Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes, and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to anomaly detection tasks in distributed environments may lead to suboptimal performance results. We propose a federated graph anomaly detection framework via contrastive self-supervised learning (CSSL) federated CSSL anomaly detection framework (FedCAD) to address these challenges. FedCAD updates anomaly node information between clients via federated learning (FL) interactions. First, FedCAD uses pseudo-label discovery to determine the anomaly node of the client preliminarily. Second, FedCAD employs a local anomaly neighbor embedding aggregation strategy. This strategy enables the current client to aggregate the neighbor embeddings of anomaly nodes from other clients, thereby amplifying the distinction between anomaly nodes and their neighbor nodes. Doing so effectively sharpens the contrast between positive and negative instance pairs within contrastive learning, thus enhancing the efficacy and precision of anomaly detection through such a learning paradigm. Finally, the efficiency of FedCAD is demonstrated by experimental results on four real graph datasets.

The rapid development of technology such as virtual reality and augmented reality, coupled with the reduced direct contact due to the COVID-19 pandemic, has led to the emergence of a more advanced mode of interaction: air handwriting. This new form of human-computer interaction allows users to input text by writing in the air freely. However, deploying and applying existing air handwriting recognition systems in real-world scenarios still presents challenges, particularly in real-time performance, privacy protection, and label scarcity. To address these challenges, we propose a federated active learning framework called FedAWR for air handwriting recognition tasks. FedAWR utilizes distributed learning to train a shared global model in the cloud from multiple user devices at the network's edge, while keeping the user's handwritten data local to ensure privacy. In addition, FedAWR employs an interactive active learning strategy to collect user-provided annotations for iterative training during the online federated learning process, bootstrapping personalized models for each client. To further enhance interactivity and real-time performance, we designed a lightweight recognition model, which is integrated into FedAWR. Finally, extensive experiments were conducted on real-world air handwritten datasets to validate the superiority of FedAWR.

The rapid advancement of virtual and augmented reality technologies has catalyzed a new paradigm in consumer electronics: air writing, a form of touchless human-computer interaction with vast potential. Implementing air writing recognition systems, however, faces challenges such as label scarcity and privacy concerns. Addressing these, we propose the Federated Adaptive Pseudo-labeling Selection (FedAPS) framework, a federated semi-supervised learning approach, enhancing decision-making in consumer electronics using multi-modal data. FedAPS innovatively utilizes limited labeled data alongside extensive unlabeled data, maintaining user privacy. We employ a multi-modal data augmentation process for air writing recognition by designing an adaptive pseudo-labeling strategy. This enables clients to select the most appropriate model for pseudo-labeling based on historical local and global models and dynamic word score recommendations. We introduce a historical local-global consistency regularization to maximize knowledge extraction from unselected models when similar predictions occur. Our comprehensive evaluation on a real-world multi-modal air writing dataset shows FedAPS’s effectiveness, outperforming advanced federated semi-supervised baselines and achieving performance comparable to fully labeled federated supervised learning. This highlights its potential in enhancing data-driven decision-making for next-generation multi-modal input consumer electronics.