Principles and Components of Federated Learning Architectures

Federated learning, also known as FL, is a machine learning framework in which a significant amount of clients (such as mobile devices or whole enterprises) collaborate to collaboratively train a model while keeping decentralized training data, all overseen by a central server (such as a service provider). There are advantages in terms of privacy, security, regulations, and economy with this decentralized approach to model training. FL is not impervious to the flaws that plague conventional machine learning models, despite its seeming promise. This study offers a thorough analysis of the fundamental ideas and elements of federated learning architectures, emphasizing five important areas: communication architectures, machine learning models, data partitioning, privacy methods, and system heterogeneity. We additionally address the difficulties and potential paths for future study in the area. Furthermore, based on a comprehensive review of the literature, we present a collection of architectural patterns for federated learning systems. This analysis will help to understand the basic of Federated learning, the primary components of FL, and also about several architectural details.

Deep Learning for Automated Wound Classification And Segmentation

Wounds, such as foot ulcers, pressure ulcers, leg ulcers, and infected wounds, come up with substantial problems for healthcare professionals. Prompt and accurate segmentation is crucial for effective treatment. However, contemporary methods need an exhaustive model that is qualified for both classification and segmentation, especially lightweight ones. In this work, we tackle this issue by presenting a new architecture that incorporates U-Net, which is optimized for both wound classification and effective segmentation. We curated four extensive and diverse collections of wound images, utilizing the publicly available Medetec Dataset, and supplemented with additional data sourced from the Internet. Our model performed exceptionally well, with an F1 score of 0.929, a Dice score of 0.931 in segmentation, and an accuracy of 0.915 in classification, proving its effectiveness in both classification and segmentation work. This accomplishment highlights the potential of our approach to automating wound care management.

Introduction to Medical Imaging Informatics

Medical imaging informatics is a rapidly growing field that combines the principles of medical imaging and informatics to improve the acquisition, management, and interpretation of medical images. This chapter introduces the basic concepts of medical imaging informatics, including image processing, feature engineering, and machine learning. It also discusses the recent advancements in computer vision and deep learning technologies and how they are used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care.