An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation

Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Federated learning (FL) is a distributed machine learning scheme that allows buses to receive model updates without waiting for model training on the cloud. However, FL is vulnerable to poisoning or DDoS attacks since buses travel in public. Some work introduces blockchain to improve reliability, but the additional latency from the consensus process reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models. To address the above challenges, this paper offers a blockchain-based asynchronous federated learning scheme with a dynamic scaling factor (DBAFL). Specifically, the novel committee-based consensus algorithm for blockchain improves reliability at the lowest possible cost of time. Meanwhile, the devised dynamic scaling factor allows AFL to assign reasonable weights to stale local models. Extensive experiments conducted on heterogeneous devices validate outperformed learning performance, efficiency, and reliability of DBAFL

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

Federated learning (FL) utilizes edge computing devices to collaboratively train a shared model while each device can fully control its local data access. Generally, FL techniques focus on learning model on independent and identically distributed (iid) dataset and cannot achieve satisfiable performance on non-iid datasets (e.g. learning a multi-class classifier but each client only has a single class dataset). Some personalized approaches have been proposed to mitigate non-iid issues. However, such approaches cannot handle underlying data distribution shift, namely data distribution skew, which is quite common in real scenarios (e.g. recommendation systems learn user behaviors which change over time). In this work, we provide a solution to the challenge by leveraging smart-contract with federated learning to build optimized, personalized deep learning models. Specifically, our approach utilizes smart contract to reach consensus among distributed trainers on the optimal weights of personalized models. We conduct experiments across multiple models (CNN and MLP) and multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate that our personalized learning models can achieve better accuracy and faster convergence compared to classic federated and personalized learning. Compared with the model given by baseline FedAvg algorithm, the average accuracy of our personalized learning models is improved by 2% to 20%, and the convergence rate is about 2$\times$ faster. Moreover, we also illustrate that our approach is secure against recent attack on distributed learning.Comment: 12 pages, 9 figure

The function and regulation of heat shock transcription factor in Cryptococcus

Cryptococcus species are opportunistic human fungal pathogens. Survival in a hostile environment, such as the elevated body temperatures of transmitting animals and humans, is crucial for Cryptococcus infection. Numerous intriguing investigations have shown that the Hsf family of thermotolerance transcription regulators plays a crucial role in the pathogen-host axis of Cryptococcus. Although Hsf1 is known to be a master regulator of the heat shock response through the activation of gene expression of heat shock proteins (Hsps). Hsf1 and other Hsfs are multifaceted transcription regulators that regulate the expression of genes involved in protein chaperones, metabolism, cell signal transduction, and the electron transfer chain. In Saccharomyces cerevisiae, a model organism, Hsf1’s working mechanism has been intensively examined. Nonetheless, the link between Hsfs and Cryptococcus pathogenicity remains poorly understood. This review will focus on the transcriptional regulation of Hsf function in Cryptococcus, as well as potential antifungal treatments targeting Hsf proteins

Experimentally ruling out joint reality based on operational completeness

Whether the observables of a physical system admit real values is of fundamental importance to a deep understanding of nature. In this work, we report a device-independent experiment to confirm that the joint reality of two observables on a single two-level system is incompatible with the assumption of operational completeness, which is strictly weaker than that of preparation noncontextuality. We implement two observables on a trapped $^{171}{\rm Yb}^{+}$ ion to test this incompatibility via violation of certain inequalities derived from both linear and nonlinear criteria. Moreover, by introducing a highly controllable dephasing channel, we show that the nonlinear criterion is more robust against noise. Our results push the fundamental limit to delineate the quantum-classical boundary and pave the way for exploring relevant problems in other scenarios.Comment: 6 pages, 3 figure

System-level biological effects of extremely low-frequency electromagnetic fields: an in vivo experimental review

During the past decades, the potential effects of extremely low-frequency electromagnetic fields (ELF-EMFs) on human health have gained great interest all around the world. Though the International Commission on Non-Ionizing Radiation Protection recommended a 100 μT, and then a 200 μT magnetic field limit, the long-term effects of ELF-EMFs on organisms and systems need to be further investigated. It was reported that both electrotherapy and possible effects on human health could be induced under ELF-EM radiation with varied EM frequencies and fields. This present article intends to systematically review the in vivo experimental outcome and the corresponding mechanisms to shed some light on the safety considerations of ELF-EMFs. This will further advance the subsequent application of electrotherapy in human health