Fedssl: privacy-preserving federated self-supervised learning with differential privacy guarantees for heterogeneous edge environments

Authors

  • Zayyanu Yunusa Computer Science, Iconic Open University of Nigeria, Iconic Open University of Nigeria, Bakura, Nigeria.

Keywords:

Federated Learning, Self-Supervised Learning, Differential Privacy, Contrastive Learning, Non-IID, Intelligence.

Abstract

In this study, we delve into the significant impact of AI, investigating its multifaceted consequences on society. In a rapidly evolving digital landscape, the integration of artificial intelligence has emerged as a transformative force in reshaping human progress and well-being. Drawing from historical perspectives, we trace the evolution of AI and its transformative journey. Our research aims to comprehensively analyze approaches for fostering responsible AI growth while mitigating potential hazards. By illuminating both the promise and perils of AI, this study contributes to informed decision-making in the unfolding AL era. This research explores the innovative utilization of AI technologies to optimize individual and societal gains while concurrently influencing the reconstruction of prevailing social norms. By harnessing the power of AI, we embark on a journey toward a new era, characterized by more efficient problem-solving, enhanced decision-making, and the redefinition of traditional social paradigms. This study investigates the multifaceted impacts of AI on human development, offering insights into its potential to revolutionize our world and foster a future marked by unprecedented advancements in progress, wellness, and social transformation.

Published

2025-06-02

How to Cite

Zayyanu Yunusa. (2025). Fedssl: privacy-preserving federated self-supervised learning with differential privacy guarantees for heterogeneous edge environments. Journal of Artificial Intelligence,Machine Learning and Neural Network , 5(1), 151–160. Retrieved from https://journal.hmjournals.com/index.php/JAIMLNN/article/view/6330

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