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The study explores federated learning as a solution to data privacy issues in distributed machine learning.

February 19, 2026Open Access

Federated Learning and Data Privacy in Distributed Machine Learning

Key Points

The study explores federated learning as a solution to data privacy issues in distributed machine learning.
Overview of federated learning frameworks
Examination of privacy risks and mitigation strategies
Analysis of theoretical foundations and operational mechanisms
Discussion of applications in sensitive sectors
Evaluation of compliance with data protection regulations
Federated learning enables model training without direct data sharing
Mitigation strategies like differential privacy enhance security
Identifies challenges such as model inversion and poisoning attacks
Highlights practical applications across various industries
Demonstrates a balance between innovation and regulatory compliance

Abstract

In recent years, the widespread adoption of artificial intelligence and machine learning systems has significantly transformed decision-making processes across critical domains such as healthcare, finance, public administration, and digital platforms. While the effectiveness of these systems largely depends on access to large-scale and high-quality data, the increasing strategic value of data has intensified concerns regarding privacy, security, and regulatory compliance. Traditional centralized machine learning architectures, which rely on aggregating data into a single repository, pose substantial technical, legal, and ethical risks, particularly under strict data protection regimes such as the European Union’s General Data Protection Regulation (GDPR). Federated learning has emerged as a promising alternative within the framework of distributed machine learning by enabling collaborative model training without direct data sharing. By keeping data localized and exchanging only model updates, federated learning addresses key challenges related to data silos, data sovereignty, and privacy-by-design principles. This article provides a comprehensive analysis of federated learning from a data privacy perspective, examining its theoretical foundations, operational mechanisms, and major variants, including horizontal and vertical federated learning. Furthermore, the study explores practical applications of federated learning in sensitive sectors such as healthcare, finance, Internet of Things (IoT) ecosystems, and public governance, highlighting both its advantages and inherent limitations. Particular attention is given to privacy risks such as model inversion and poisoning attacks, as well as mitigation strategies including differential privacy and secure aggregation. Finally, the article assesses the relevance of federated learning within the evolving legal and institutional environment of the European Union, arguing that federated learning represents a strategic balance between technological innovation, regulatory compliance, and responsible artificial intelligence development.

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