Federated learning is a promising new technology in the field of IoT intelligence. However, exchanging model-related data in federated learning may leak the sensitive information of participants. To address this problem, we propose a novel privacy-preserving FL framework based on an innovative chained secure multi-party computing technique, named Chain-PPFL. Our scheme mainly leverages two mechanisms: Single-Masking mechanism which protects information exchanged between participants; Chained-Communication mechanism which enables masked information to be transferred between participants with a serial chain frame. We conduct extensive simulation-based experiments using two public datasets (MNIST and CIFAR-100) by comparing both training accuracy and leak-defence with other state-of-the-art schemes. We set two data sample distributions (IID and Non-IID) and three training models (CNN, MLP and L-BFGS) in our experiments. The experimental results demonstrate that the Chain-PPFL scheme can achieve a practical privacy-preservation (equivalent to differential privacy with approaching zero) for federated learning with some cost of communication and without impairing the accuracy and convergence speed of the training model.

With recent developments in communication technologies, vehicular networks have become a reality with various applications. However, the cybersecurity aspect of vehicular networks is still an open issue that needs to be addressed with novel defence mechanisms against attacks. This paper first presents the state-of-the-art communication technologies in vehicular networks (either inter-vehicle networking or in-vehicle networking) along with their applications. Then we explore novel technologies including machine learning and blockchain as cybersecurity defence mechanisms in vehicular networks. Based on the extensive survey, we highlight some insights for future research to secure vehicular networks.

Traditional recommendation methods suffer from limited performance, which can be addressed by incorporating abundant auxiliary/side information. This article focuses on a personalized music recommender system that incorporates rich content and context data in a unified and adaptive way to address the abovementioned problems. The content information includes music textual content, such as metadata, tags, and lyrics, and the context data incorporate users' behaviors, including music listening records, music playing sequences, and sessions. Specifically, a heterogeneous information network (HIN) is first presented to incorporate different kinds of content and context data. Then, a novel method called content- and context-aware music embedding (CAME) is proposed to obtain the low-dimension dense real-valued feature representations (embeddings) of music pieces from HIN. Especially, one music piece generally highlights different aspects when interacting with various neighbors, and it should have different representations separately. CAME seamlessly combines deep learning techniques, including convolutional neural networks and attention mechanisms, with the embedding model to capture the intrinsic features of music pieces as well as their dynamic relevance and interactions adaptively. Finally, we further infer users' general musical preferences as well as their contextual preferences for music and propose a content- and context-aware music recommendation method. Comprehensive experiments as well as quantitative and qualitative evaluations have been performed on real-world music data sets, and the results show that the proposed recommendation approach outperforms state-of-the-art baselines and is able to handle sparse data effectively.