1. Introduction
Privacy concerns drive federated learning adoption...
2. Federated Learning Framework
System architecture and algorithms...
3. Privacy Guarantees
Differential privacy implementation...
4. Experimental Results
Performance versus centralized learning...
5. Conclusion
Federated learning enables privacy-preserving AI...
References
[1] McMahan, B. et al. (2017). Federated Learning.
[2] Dwork, C. (2006). Differential Privacy.
Federated Learning for Privacy-Preserving Machine Learning
Sarah Johnson
MIT
DOI: 10.1234/journal.v3.1.013
Published: January 15, 2025
Research Articles
Abstract
This research investigates federated learning as a solution for privacy-preserving machine learning. We implement and evaluate a federated learning system across 100 edge devices, demonstrating that models can achieve 95% of centralized performance while maintaining data privacy. The system includes differential privacy guarantees.
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Article Info
-
Issue:
Vol. 3 No. 1 (2025): Current Issue -
Section:
Research Articles -
Submitted:
November 01, 2024 -
Published:
January 15, 2025 -
DOI:
10.1234/journal.v3.1.013
How to Cite
Sarah Johnson (2025). "Federated Learning for Privacy-Preserving Machine Learning." Abhidakara Jurnal, 3(1). doi:10.1234/journal.v3.1.013