RESUMO
As a decentralized training approach, horizontal federated learning (HFL) enables distributed clients to collaboratively learn a machine learning model while keeping personal/private information on local devices. Despite the enhanced performance and efficiency of HFL over local training, clues for inspecting the behaviors of the participating clients and the federated model are usually lacking due to the privacy-preserving nature of HFL. Consequently, the users can only conduct a shallow-level analysis of potential abnormal behaviors and have limited means to assess the contributions of individual clients and implement the necessary intervention. Visualization techniques have been introduced to facilitate the HFL process inspection, usually by providing model metrics and evaluation results as a dashboard representation. Although the existing visualization methods allow a simple examination of the HFL model performance, they cannot support the intensive exploration of the HFL process. In this article, strictly following the HFL privacy-preserving protocol, we design an exploratory visual analytics system for the HFL process termed HFLens, which supports comparative visual interpretation at the overview, communication round, and client instance levels. Specifically, the proposed system facilitates the investigation of the overall process involving all clients, the correlation analysis of clients' information in one or different communication round(s), the identification of potential anomalies, and the contribution assessment of each HFL client. Two case studies confirm the efficacy of our system. Experts' feedback suggests that our approach indeed helps in understanding and diagnosing the HFL process better.
Assuntos
Gráficos por Computador , Aprendizado de Máquina , Humanos , RetroalimentaçãoRESUMO
Quantum key distribution (QKD) is a technology that allows secure key exchange between two distant users. A widespread adoption of QKD requires the development of simple, low-cost, and stable systems. However, implementation of the current QKD requires a complex self-alignment process during the initial stage and an additional hardware to compensate the environmental disturbances. In this study, we present the implementation of a simple QKD with the help of a stable transmitter-receiver scheme, which simplifies the self-alignment and is robust enough to withstand environmental disturbances. In case of the stability test, the implementation system is able to remain stable for 48 h and exhibits an average quantum bit error rate of less than 1% without any feedback control. The scheme is also tested over a fiber spool, obtaining a stable and secure finite key rate of 7.32k bits per second over a fiber spool extending up to 75 km. The demonstrated long-term stability and obtained secure key rate prove that our method of implementation is a promising alternative for practical QKD systems, in particular, for CubeSat platform and satellite applications.
RESUMO
Quaternized chitosan/organic montmorillonite (QCS/OMMT) nanocomposites were synthesized under microwave irradiation. XRD and TEM analyses confirmed that QCS intercalated into the interlayer of OMMT and clay layers distributed uniformly in QCS matrix. QCS/OMMT nanocomposites were used as retention and drainage-aid agents in pulp suspension, during which the interface behavior of positively charged QCS/OMMT nanocomposites on negatively charged cellulosic substrate and CaCO3 substrate was investigated. With the addition of QCS/OMMT nanocomposites, the particle size of cellulosic substrate increased, while the beating degree and the total residual carbohydrate concentration decreased. The results indicated that QCS/OMMT nanocomposite made a difference in paper making process through the charge patch interaction. Moreover, QCS/OMMT nanocomposites had a strong interaction with CaCO3, which was significant in fiber fines retention and paper production. When the mass ratio of QCS to OMMT was 8:1, the QCS/OMMT nanocomposite demonstrated the strongest retention and drainage-aid effect.