Enhancing Security in Federated Learning: Designing Distributed Data Science Algorithms to Reduce Cyber Threats
Year: 2025
Type: article
Abstract: As data privacy regulations tighten and distributed computing proliferates, federated learning (FL) has emerged as a transformative paradigm that enables collaborative model training without centralized data aggregation. However, while federated learning offers inherent privacy advantages, it also introduces new cybersecurity vulnerabilities, including poisoning attacks, model inversion, and adversarial manipulations. Traditional cybersecurity methods often fall short when applied to decentralized architectures, necessitating novel, robust defense mechanisms tailored specifically for federated environments. This paper investigates the intersection of federated learning, distributed data science, and advanced cybersecurity, focusing on the design of resilient algorithms that proactively mitigate cyber threats without compromising model accuracy or communication efficiency. Key strategies discussed include the integration of robust aggregation techniques, differential privacy mechanisms, homomorphic encryption, and secure multi-party computation within FL frameworks. Additionally, the role of anomaly detection algorithms, trust scoring systems, and blockchain-based audit trails in enhancing the integrity and accountability of federated networks is critically examined. Drawing on case studies across sectors such as healthcare, finance, and autonomous vehicles, the paper illustrates how distributed algorithmic defenses can effectively resist sophisticated attacks while preserving federated learning’s core advantages. The analysis also highlights trade-offs between security, computational overhead, and model convergence rates. Ultimately, the paper argues that the future scalability and trustworthiness of federated learning depend on embedding security principles directly into the data science algorithms that drive distributed intelligence, setting the stage for more resilient, privacy-preserving AI ecosystems.
Authors: Masunda, Martha, Ajayi, Rhoda
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930804160
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