Federated Learning (FL) is an emerging distributed machine learning paradigm that provides privacy guarantees for training robust models on distributed clients. The primary challenge of FL is data heterogeneity, which slows down model convergence and degrades model performance. Knowledge distillation has recently demonstrated effectiveness in addressing this challenge. However, these approaches neglect the statistical heterogeneity in local models and the uncertainty of the data distribution in the global model, which results in the ensemble knowledge cannot be fully utilized to guide local model learning. In this work, we propose an unsupervised knowledge distillation method migrating the local class-level pseudo-data sample scheme in the server for fine-tuning the global model. Specifically, we provide the conditional autoencoder for each client to maintain a dynamic generator in the server, which ensembles the client’s class-level information. The proposal produces an auxiliary dataset representing the global class-level distribution to regulate the local model as an inductive knowledge bias, and employs unsupervised knowledge distillation to enhance the aggregated model’s performance. The extensive experiments show that our proposal significantly outperforms the current state-of-the-art FL algorithms and can be integrated as a flexible plugin into existing FL optimization algorithms to enhance model performance.

The Modbus protocol serves as a fundamental element in modern computer network systems, with Modbus Transmission Control Protocol (TCP) being particularly vital in the realms of edge computing and industrial computing. Although User Datagram Protocol (UDP) is frequently acknowledged for its superior transmission speed relative to TCP, it is deficient in the reliability that TCP offers. Modbus utilizes the attributes of TCP to ensure accurate data transmission; however, it exhibits inherent limitations when managing large data volumes, which negatively impacts the performance of the communication link. To address this challenge, we propose an innovative approach referred to as Modbus UDP over Time-Sensitive Networking (TSN). This method not only significantly improves transmission performance, but also leverages the benefits of TSN to rectify the reliability shortcomings associated with UDP. Experimental results obtained from the testing platform indicate that this approach can markedly enhance the capacity for lossless data transmission.

With the development of automobile intelligence and connectivity, Intelligent and Connected Vehicle (ICV) is an inevitable trend in the transformation and upgrading of the automotive industry. The maturity of any advanced technology is inseparable from a large number of test verifications, especially the research and application of automotive technology require a large number of reliable tests for evaluation and confirmation. Therefore, the ICV Test Site (ICVTS) will become a key deployment area. In this paper, we analyze the development status of ICVTS outside and within China, summarize the shortcomings of the existing test sites, and put forward some targeted suggestions, in an effort to guide the development and construction of ICVTS towards the path that seems to be most promising.