Federated learning (FL) enables participants to collaboratively train models without compromising the privacy of their local data. However, it remains vulnerable to threats such as Byzantine attacks and privacy leaks. These challenges are inherently interdependent: privacy-preserving mechanisms limit access to model updates to protect private information, whereas Byzantine-robust techniques need such access for thorough analysis. Existing research has developed FL for protecting privacy and resisting Byzantine attacks, but most of these methods are effective only under the honest majority premise or based on the non-collusion assumption among servers, and a few of them require the assistance of trusted third parties (TTPs). To address these challenges, we propose a Byzantine-robust and privacypreserving FL framework (BRPPFL), which identifies Byzantine attackers in the compressed domain of compressed sensing (CS) and has the advantage of low communication overhead. To mitigate privacy leaks, BRPPFL combines secure two-party computation (2PC) protocols with homomorphic encryption (HE) primitives to create a privacy-preserving secure aggregation protocol. Additionally, it minimizes computational overhead by offloading complex operations to the offline phase. Experimental results demonstrate that BRPPFL can accurately and efficiently identify Byzantine attackers while protecting privacy. Even at an extreme compression rate (CR) of 0.1, our framework maintains acceptable defense performance.

Prior research on emotion recognition in extended reality (XR) has faced challenges due to the occlusion of facial expressions by Head-Mounted Displays (HMDs). This limitation hinders accurate Facial Expression Recognition (FER), which is crucial for immersive user experiences. This study aims to overcome the occlusion challenge by integrating physiological signals with partially visible facial expressions to enhance emotion recognition in XR environments. We employed a multi-task approach, utilizing a feature-level fusion to fuse Electroencephalography (EEG) and Galvanic Skin Response (GSR) signals with occluded facial expressions. The model predicts valence and arousal simultaneously from both macro-and micro-expression. Our method demonstrated improved accuracy in emotion recognition under partial occlusion conditions. The integration of temporal physiological signals with other modalities significantly enhanced performance, particularly for half-face emotion recognition. The study presents a novel approach to emotion recognition in XR, addressing the limitations of facial occlusion by HMDs. The findings suggest that physiological signals are vital for interpreting emotions in occluded scenarios, offering potential for real-time applications and advancing social XR applications.

Previous research has demonstrated the potential of Augmented Reality in enhancing psychological comfort in Human-Robot Interaction (AR-HRI) through shared robot intent, enhanced visual feedback, and increased expressiveness and creativity in interaction methods. However, the challenge of selecting interaction methods that enhance physical comfort in varying scenarios remains. This study purposes a dynamic dual-layer interaction adjustment mechanism to improve user comfort and interaction efficiency. The mechanism comprises two models: an general layer model, grounded in ergonomics principles, identifies appropriate areas for various interaction methods; a individual layer model predicts user discomfort levels using physiological signals. Interaction methods are dynamically adjusted based on discomfort level changes, enabling the system to adapt to individual differences and dynamic changes, thereby reducing misjudgments and enhancing comfort management. The mechanism's success in authoring tasks validates its effectiveness, significantly advancing AR-HRI and fostering more comfortable and enhancing efficient human-centered interactions.