جستجوی مقالات مرتبط با کلیدواژه « consensus algorithm » در نشریات گروه « فناوری اطلاعات »

Federated learning is a distributed data analysis approach used in many IoT applications, including IoMT, due to its ability to provide acceptable accuracy and privacy. However, a critical issue with Federated learning is the poisoning attack, which has severe consequences on the accuracy of the global model caused by the server's lack of access to raw data. To deal with this problem effectively, a distributed federated learning approach involving blockchain technology is proposed. Using the consensus mechanism based on reputation-based verifier selection, verifiers are selected based on their honest participation in identifying compromised clients. This approach ensures that these clients are correctly identified and their attack is ineffective. The proposed detection mechanism can efficiently resist the data poisoning attack, which significantly improves the accuracy of the global model. Based on evaluation, the accuracy of the global model is compared with and without the proposed detection mechanism that varies with the percentage of poisonous clients and different values for the fraction of poisonous data. In addition to the stable accuracy range of nearly 93%, the accuracy of our proposed detection mechanism is not affected by the increase of α in different values of β. </span>

In this manuscript we suggest a fast adaptive distributed method for maximum likelihood approximation (MLA) in multiple view object localization problem. For this purpose, we use "up to scale" property of projective geometry and by defining coefficients for convergence criterion, we increase the convergence speed of the consensus algorithm. We try to present a mathematical model for the problem. We use two types of error function. The proposed method uses maximum likelihood for obtaining its best parameters. Our approach utilizes "up to scale" property in projective geometry to reach the consensus quickly. The difference between node's values and meanwhile consensus values are evaluated by two error functions. To estimate consensus value in the second error function, we used local weighted average of each node. At the last of the paper, we prove our claims by experimental results.