Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale graphs. Traditional multi-task learning methods often neglect these graph structures, relying solely on individual user and item embeddings. However, disregarding graph structures overlooks substantial potential for improving performance. In this paper, we introduce the Macro Graph of Experts (MGOE) framework, the first approach capable of leveraging macro graph embeddings to capture task-specific macro features while modeling the correlations between task-specific experts. Specifically, we propose the concept of a Macro Graph Bottom, which, for the first time, enables multi-task learning models to incorporate graph information effectively. We design the Macro Prediction Tower to dynamically integrate macro knowledge across tasks. MGOE has been deployed at scale, powering multi-task learning for a leading billion-scale recommender system, Alibaba. Extensive offline experiments conducted on three public benchmark datasets demonstrate its superiority over state-of-the-art multi-task learning methods, establishing MGOE as a breakthrough in multi-task graph-based recommendation. Furthermore, online A/B tests confirm the superiority of MGOE in billion-scale recommender systems.

Recently, the explosive development of unmanned aerial vehicles (UAVs) promotes its wide application in various services such as package delivery, traffic monitoring. However, due to the high-speed movement, current UAVs usually adopts the unsecure channel without the complicated authentication mechanism to ensure real-time communication. In this paper, we introduce a certificateless authentication scheme with fuzzy batch verification (CLFBV) to achieve once-for-all verification of parallel messages and ensure the real-time secure communication of UAVs. CLBFV defines the error tolerance property for authenticated communication, which allows a tolerance threshold for the messages that are unable to pass authentication. In addition, our proposed scheme is proved to be secure and existentially unforgeable under the chosen message attack and fuzzy identity attack in the random oracle model. The efficiency analysis shows that CLBFV is more efficient and feasible than other existing batch verification schemes.