Federated Learning: Navigating the Landscape of Collaborative Intelligence

• DOI: 10.3390/electronics13234744
• Publication Date: 2024-12-03T09:04:04Z
• Authors: Konstantinos Lazaros; Dimitrios E. Koum...; Aristidis G. Vrah...; Sotiris Kotsiantis
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

As data become increasingly abundant and diverse, their potential to fuel machine learning models is vast. However, traditional centralized approaches, which require aggregating into a single location, face significant challenges. Privacy concerns, stringent protection regulations like GDPR, the high cost of transmission hinder feasibility centralizing sensitive from disparate sources such as hospitals, financial institutions, personal devices. Federated Learning addresses these issues by enabling collaborative model training without requiring raw leave its origin. This decentralized approach ensures privacy, reduces costs, allows organizations harness collective intelligence distributed while maintaining compliance with ethical legal standards. review delves FL’s current applications reshape IoT systems more collaborative, privacy-centric, flexible frameworks, aiming enlighten motivate those navigating confluence advancements.