In recent years， federated learning has become a new way to solve the problems of data island and privacy leakage in machine learning. Federated learning architecture does not require multiple parties to share data resources， in which participants only needed to train local models on local data and periodically upload parameters to the server to update the global model， and then a machine learning model can be built on large-scale global data. Federated learning architecture has the privacy-preserving nature and is a new scheme for large-scale data machine learning in the future. However， the parameter interaction mode of this architecture may lead to data privacy disclosure. At present， strengthening the privacy-preserving mechanism in federated learning architecture has become a new research hotspot. Starting from the privacy disclosure problem in federated learning， the attack models and sensitive information disclosure paths in federated learning were discussed， and several types of privacy-preserving techniques in federated learning were highlighted and reviewed， such as privacy-preserving technology based on differential privacy， privacy-preserving technology based on homomorphic encryption， and privacy-preserving technology based on Secure Multiparty Computation （SMC）. Finally， the key issues of privacy protection in federated learning were discussed， the future research directions were prospected.
The supply chain faces many challenges in the development process， including how to ensure the authenticity and reliability of information as well as the security of the traceability system in the process of product traceability， the security of products in the process of logistics， and the trust management in the financing process of small and medium enterprises. With characteristics of decentralization， immutability and traceability， blockchain provides efficient solutions to supply chain management， but there are some technical challenges in the actual implementation process. To study the applications of blockchain technology in the supply chain， some typical applications were discussed and analyzed. Firstly， the concept of supply chain and the current challenges were briefly introduced. Secondly， problems faced by blockchain in three different supply chain fields of information flow， logistics flow and capital flow were described， and a comparative analysis of related solutions was given. Finally， the technical challenges faced by blockchain in the practical applications of supply chain were summarized， and future applications were prospected.
Current mobile recommendation systems limit the real role of location information, because the systems just take location as a general property. More importantly, the correlation of location and the boundary of activities of users have been ignored. According to this issue, personalized recommendation technique for mobile life services based on location cluster was proposed, which considered both user preference in its location cluster and the related weight by forgetting factor and information entropy. It used fuzzy cluster to get the location cluster, then used forgetting factor to adjust the preference of the service resources in the location cluster. Then the related weight was obtained by using probability distribution and information entropy. The top-N recommendation set was got by matching the user preference and service resources. As a result, the algorithm can provide service resources with high similarities with user preference. This conclusion has been verified by case study.
To meet the requirements of the military and merchant marine radar simulation, and enhance the simulation reality of radar image, a real-time scan simulation approach based on sector-banded texture blending model was presented to simulate highly realistic radar echo image. In this method, Electronic Navigation Chart (ENC) was regarded as the resource data of the radar echo signal, and according to the principle of the radar echo, the sector-banded texture blending algorithm was proposed to replace the traditional radar image simulation method based on the pixel-scan model and generate the radar echo texture data. Based on that, the simulation models of the radar echo signal processing were presented to implement the basic functions of the marine radar, such as gain adjustment, sea clutter suppression and rain/snow clutter suppression. The experimental results show the proposed approach improves distinctly the efficiency and effectiveness of the radar echo simulation, and it is a promising means to address the problem of radar and Electronic Chart Display Information System (ECDIS) simulation.
