Under stringent privacy constraints, whether federated recommendation systems can achieve group fairness remains an inadequately explored question. Taking gender fairness as a representative issue, we identify three phenomena in federated recommendation systems: performance difference, data imbalance, and preference disparity. We discover that the state-of-the-art methods only focus on the first phenomenon. Consequently, their imposition of inappropriate fairness constraints detrimentally affects the model training. Moreover, due to insufficient sensitive attribute protection of existing works, we can infer the gender of all users with 99.90% accuracy even with the addition of maximal noise. In this work, we propose Privacy-Preserving Orthogonal Aggregation (PPOA), which employs the secure aggregation scheme and quantization technique, to prevent the suppression of minority groups by the majority and preserve the distinct preferences for better group fairness. PPOA can assist different groups in obtaining their respective model aggregation results through a designed orthogonal mapping while keeping their attributes private. Experimental results on three real-world datasets demonstrate that PPOA enhances recommendation effectiveness for both females and males by up to 8.25% and 6.36%, respectively, with a maximum overall improvement of 7.30%, and achieves optimal fairness in most cases. Extensive ablation experiments and visualizations indicate that PPOA successfully maintains preferences for different gender groups.

As deep neural network (DNN) has made significant advancements across various fields, systematically testing DNN has become increasingly crucial. To uncover potential faults within DNN, a vast number of test cases and their correct labels are required, but the process of labeling is time-consuming and labor-intensive. To alleviate the burden on developers, test case selection techniques for DNN models have been proposed, assisting in the selection of test cases from large datasets that are more likely to reveal model faults. In this study, we introduce an efficient adaptive test case selection method based on the principle of uniform distribution of test cases, named EATS. In addition, we propose a test case optimization method and an image similarity calculation method. The optimization method can save time during the test case selection process, while the image similarity calculation method computes the degree of difference between images based on model uncertainty. EATS leverages the model’s uncertainty to achieve a more uniform distribution of selected test cases, aiming to select test cases that can induce a diversity of model fault predictions, thereby optimizing model performance. We conducted comparative experiments of EATS and other test case selection strategies on four common datasets and their corresponding DNN models. The experimental results show that EATS outperforms other methods in terms of the uniformity of test case distribution, diversity of errors discovered, and model optimization. It also demonstrates excellent time efficiency.

Privacy protection in recommendation systems is gaining increasing attention, for which federated learning has emerged as a promising solution. Current federated recommendation systems grapple with high communication overhead due to sharing dense global embeddings, and also poorly reflect user preferences due to data heterogeneity. To overcome these challenges, we propose a two-stage Federated Low-rank Coordinated Adaptation (FedLoCA) framework to decouple global and client-specific knowledge into low-rank embeddings, which significantly reduces communication overhead while enhancing the system’s ability to capture individual user preferences amidst data heterogeneity. Further, to tackle gradient estimation inaccuracies stemming from data sparsity in federated recommendation systems, we introduce an adversarial gradient projected descent approach in low-rank spaces, which significantly boosts model performance while maintaining robustness. Remarkably, FedLoCA also alleviates performance loss even under the stringent constraints of differential privacy. Extensive experiments on various real-world datasets demonstrate that FedLoCA significantly outperforms existing methods in both recommendation accuracy and communication efficiency.

Augmented reality (AR) applications based on conventional approaches lack the adaptability to cater to different scene requirements and address users' personalized demands effectively. This demon presents ChatGPT-AR, a ChatGPT-powered near real-time voice-to-AR mobile application system, that can create different 3D-augmented spaces using voice commands. Further, ChatGPT-AR enables near real-time contextual editions in AR fashion.

Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, Haifeng Wang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.