In the era of edge computing, where data is generated and processed at the network's edge, ensuring privacy and scalability in machine learning models is paramount. Federated Learning (FL) addresses these challenges by allowing multiple edge devices to collaboratively train models without sharing raw data. This paper investigates the implementation of FL in distributed cloud systems, highlighting its role in preserving data privacy and improving scalability. We analyze various FL algorithms, such as Federated Averaging (FedAvg) and Hybrid Federated Dual Coordinate Ascent (HyFDCA), assessing their effectiveness in edge computing contexts. Additionally, we explore techniques like inverse distance aggregation to handle non-IID data distributions and discuss the trade-offs between communication and computation in FL frameworks. Through comprehensive analysis and experimentation, this study provides insights into optimizing FL for edge computing, paving the way for more secure and scalable machine learning applications in distributed cloud environments.
Kenji Sato· International Journal of Art...· 0 citations
AFE is an automated feature engineering system that has become a key enabler to scalable and high-performing machine learning systems with scalable systems that run on tabular data. The conventional feature engineering makes excessive use of domain, trial and error and iterative optimization, which are computed consuming time, error prone and hard to repeat. As data-driven applications in finance, healthcare, manufacturing, and e-commerce have been exponentially increasing, there is an increasing need in automated, systematic, and reliable methods of features construction. The overall objective of automated Feature Engineering methods is to generate, transform, select, and optimize features adventurously, from raw tabular data, with minimal human intervention, and at a higher predictive efficiency. The paper contains a complete detailed analysis of automated feature engineering approaches to tabular data with references to their theoretical principles, algorithmic approaches, and real-world examples. The paper expounds on rule construction based feature construction, statistical construction, deep learning based representation, evolutionary learning, and reinforcing learning methods, and end to end AutoML. An intricate literature review shows major achievements, comparisons, and unresolved issues. The suggested methodology defines the three branches of feature generation, selection and evaluation as a single automated pipeline via mathematical representation and algorithmic processes. The effectiveness of automated feature engineering is proved by experimental results that show that the method can enhance the accuracy and robustness of models as well as improve generalization with respect to the multiple benchmark datasets. Lastly, issues of limitations, interpretability, computational trade-offs, and research directions are discussed in the paper. The given publication meets the IEEE publication standards and offers well-organized, high-quality information to a researcher or an organization practitioner dealing with tabular data analytics.
Yuki Tanaka, Kenji Sato· International Journal of Art...· 0 citations
Robotic Process Automation (RPA) has been a transformative technology that allows companies to automate plastic-based tasks that are repetitive and basis rule-based in heterogeneous information systems without having to change the underlying infrastructure. Since organizations are under growing pressure to enhance both operational efficiency, accuracy, and scalability, RPA provides a cost-effective solution to the digital transformation process since it simulates human interactions with programs. The following paper is a detailed analysis of the concept of RPA within the current enterprise processes in terms of its architecture, deployment patterns, and quantifiable business outcomes. The paper is meticulously conducting a scientific review of literature to determine existing trends, advantages, challenges, and gaps in the present research on RPA. It suggests using the structured methodology that encompasses the process discovery, bot design, orchestration, and governance mechanisms according to the enterprise standards. Measures of performance like reduction in execution time, minimization in error rate, cost savings, and return on investment are measured to check effectiveness. The convergence of RPA and artificial intelligence and machine learning is also discussed and results in intelligent automation that has the potential to process semi-structured and unstructured information. At the end of the paper, the authors establish the main issues associated with scalability, security, and maintainability and show future research perspectives of sustainable adoption of RPA in large-scale enterprise settings.
Kenji Sato, Aiko Yamamoto· International Journal of Mod...· 0 citations