Abstract
The exponential growth of data in the digital era has driven unprecedented advances in artificial intelligence and machine learning. However, the centralized collection of user data has raised profound concerns over privacy, ownership, and security. Traditional machine learning frameworks require the aggregation of raw data into centralized repositories for training, creating vulnerabilities to breaches, misuse, and unauthorized access. Federated learning (FL) has emerged as a transformative paradigm that addresses these challenges by enabling collaborative model training across decentralized devices or servers without transferring raw data. Each participant trains the model locally and shares only model updates, thereby preserving privacy and reducing communication overhead. This distributed learning approach has found extensive applications in healthcare, finance, telecommunications, and the Internet of Things, where sensitive data cannot be centralized. By combining the strengths of edge computing, encryption, and differential privacy, federated learning ensures data sovereignty and regulatory compliance. This paper explores the conceptual foundations, architectures, and applications of federated learning, emphasizing its role in enhancing data privacy and security in distributed AI models. It also discusses the major challenges of scalability, heterogeneity, communication efficiency, and adversarial robustness, while outlining future directions toward sustainable and trustworthy collaborative intelligence.
Keywords
Federated Learning
Data Privacy
Distributed Artificial Intelligence
Edge Computing
Secure Aggregation
Differential Privacy
Decentralized Machine Learning
Model Update Encryption
Data Sovereignty
Trustworthy AI
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