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Enrique Tomás Martínez Beltrán
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Enrique Tomás Martínez Beltrán

Ph.D. student at the University of Murcia working at the intersection of federated learning, cybersecurity, and privacy-preserving AI for real-world systems.

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Enrique Tomás Martínez Beltrán. All rights reserved.

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Blog & Technical Notes

Technical articles on federated learning, decentralized AI, robustness, and applied cybersecurity.

NEBULA: A Platform for Decentralized Federated Learning
March 13, 20251 year ago·12 min read

NEBULA: A Platform for Decentralized Federated Learning

This comprehensive guide explores NEBULA, a revolutionary platform for decentralized federated learning, from basic concepts to advanced applications in healthcare, IoT, and cybersecurity, with practical implementation examples.

Federated LearningAIPrivacyDecentralized SystemsNEBULA
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Federated Learning: Revolutionizing AI Without Compromising Privacy
January 29, 20242 years ago·8 min read

Federated Learning: Revolutionizing AI Without Compromising Privacy

Explore how federated learning is transforming the AI landscape by enabling collaborative model training without sharing raw data, preserving privacy while advancing machine learning capabilities.

Federated LearningPrivacyAIDistributed SystemsCybersecurity
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Decentralized Federated Learning: A New Era in Artificial Intelligence
September 15, 20233 years ago·15 min read

Decentralized Federated Learning: A New Era in Artificial Intelligence

Explore the transformative world of decentralized federated learning, from its mathematical foundations to real-world applications in cybersecurity and beyond.

DFLFederated LearningAIPrivacyDecentralization
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Trending Topics

Federated LearningAIPrivacyDecentralized SystemsNEBULADistributed Systems

Research Notes

Occasional updates on decentralized federated learning, secure AI systems, and ongoing research work.

Selected Publications

2026

Asynchronous Cache-based Aggregation with Fairness and Filtering for Decentralized Federated Learning

Computer Networks

2026

Decentralized Federated Learning with Multimodal Prototypes for Heterogeneous Data

Information Fusion