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Volume 21, Issue 3 (12-2024)
Federated Learning (FL) is an innovative machine learning paradigm that tackles the challenge of data island while safeguarding data privacy. It enables decentralized model training by allowing multiple clients—such as mobile devices, institutions, or organizations—to collaboratively build models without transferring local data to a central server. This paradigm gained significant attention following Google’s 2016 initiative to predict user text input on Android devices while maintaining the privacy of locally stored data.
A core feature of FL is its distributed and encrypted framework, enabling participants to contribute to a collective learning process without revealing their original data to a central entity or other participants. In recent years, FL has evolved to encompass a broader spectrum of decentralized machine learning techniques, while still maintaining privacy as a central tenet. This evolution has positioned FL as a critical technology in sectors where data privacy, security, and sovereignty are paramount.
This paper presents a systematic review of the literature on federated learning, synthesizing insights from review articles, Books, key documents, and published research. The review is structured as follows:
Overview of Federated Learning: This section introduces the foundational concepts of FL, detailing its origins, core principles, and operational processes. The decentralized structure and privacy-preserving techniques employed in FL are examined, along with real-world applications as examples.
Algorithms and Evolution: This section explores the state-of-the-art algorithms driving FL and traces their development over time. Key innovations in aggregation techniques, optimization methods, and client-server communication protocols are highlighted, demonstrating how they have enhanced FL's scalability and efficiency.
Classification and Applications of FL Architectures: Federated learning architectures are categorized into three main types: horizontal federated learning, vertical federated learning, and federated transfer learning. This section analyzes the application of these architectures across various domains, highlighting their distinctive features and associated challenges.
Applications in IoT, Smart Cities, and Healthcare: Using selected case studies, this section evaluates the deployment of FL in the Internet of Things (IoT), smart cities, and healthcare. It assesses how FL enhances data privacy, security, and operational efficiency in these domains, focusing on practical implementations.
Comparative Analysis: This section offers a comparative evaluation of the various methods and algorithms used in the aforementioned fields, identifying their relative strengths and weaknesses. Special attention is given to the challenges posed by large-scale FL deployments, including communication overhead, data heterogeneity, and model convergence.
Federated Learning and Related Technologies: This section explores the integration of FL with related technologies, such as federated deep learning and federated blockchain, particularly within the context of the Industrial Internet of Things (IIoT). The potential of these technologies to improve storage, data management, and resource optimization is discussed in detail.
Challenges and Future Directions: The final section addresses the ongoing challenges facing FL, including scalability, model accuracy, communication costs, and compliance with regulatory frameworks. Additionally, it proposes future research directions aimed at improving the practicality and widespread adoption of FL in industrial and commercial applications.
This systematic review provides a comprehensive examination of federated learning’s current state, including its foundational concepts, applications, and challenges. It also outlines a forward-looking perspective on the advancements needed to establish FL as a key technology in privacy-centric, decentralized machine learning.
Volume 22, Issue 2 (9-2025)
The onset of social media venues, online news media, and digital content allowed a vast volume of text and time series data to be generated which plays significant role in investors' decision-making and financial market volatility. Data extracted from these platforms provide information on public sentiments, immediate reactions to news, and informal analyses, which, if processed appropriately, can be very useful indicators in forecasting financial market trends. Billions of dollars are invested and lost, depending on correct forecasting. However, advances in deep learning, especially in large language models (LLMs) and novel time series analysis algorithms, have opened new windows to processing and analyzing this complex data. The advanced language models identify hidden patterns and nonlinear dependencies, always taking into account the context and semantic details of the text between news, market sentiments, and price fluctuations, as well as utilizing them via intelligent market analysis systems. This review analyzes the existing research trends on the relationship of text data available on websites and social networks with the behavior of financial markets, having reviewed more than 200 scientific papers published between 2006 and 2024 in a systematic manner. This study focuses on identifying advanced methods within text representation, sentiment analysis, predictive modeling, and language model applications for analyzing real-time and unstructured data. More than one information source has to be taken into consideration: (Twitter, news agencies, blogs, and specialized forums) from a perspective of credibility, data structure, and influence-on market decisions. Given the complexity of financial markets, such as stocks and forex, there is an ever-increasing demand for hybrid models capable of carrying out analyses across time-series and text data simultaneously. This paper aims to analyze the current research accomplishments, identify gaps in the research, and ultimately put forward future directions for the fields of text mining, AI, and deep learning. These directions can open up the path for the next generation of real-time and adaptive recommender, predictor, and correlation analyzer systems in the financial markets.
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