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Title	Guarding Privacy In Federated Learning: Exploring Threat Landscapes And Countermeasures With Case Studies
ID_Doc	28562
Authors	Vasa J.; Thakkar A.; Bhavsar D.; Patel P.
Year	2025
Published	Lecture Notes in Networks and Systems, 1194
DOI	http://dx.doi.org/10.1007/978-981-97-9523-9_19
Abstract	Federated learning (FL) emerges as a revolutionary paradigm for collaborative machine learning, enabling training on decentralized, privacy-sensitive data. With its distributed nature, FL holds immense potential in various domains, encompassing health care, finance, and smart cities. However, this very decentralization introduces unique vulnerabilities susceptible to diverse attacks. This paper presents a comprehensive analysis of FL, delving into its architecture, applications, and potential attack vectors. This paper investigates a range of attacks, such as poisoning attacks, Sybil attacks, and reconstruction attacks, which pose threats to the integrity of models and the privacy of data within the context of federated learning (FL). We propose robust solutions to counter these threats, employing methodologies such as differential privacy, secure aggregation, and adversarial training. Through rigorous analysis and innovative solutions, our study aims to strengthen the implementation of FL while preserving its security and privacy aspects. Additionally, we showcase the diverse applications of FL across various sectors, demonstrating its potential for transformative impact. Ultimately, this paper contributes to a comprehensive understanding of FL, facilitating its secure and ethical advancement in today's data-driven world. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Author Keywords	Applications; Federated learning; Inference attacks; Outlier detection; Poison attacks; Privacy threats; Reconstruction attack; Secure aggregation; Security

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