Federated Learning in Big Data Analytics: Challenges and Opportunities
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V1I2P101Keywords:
Federated Learning, Big Data Analytics, Privacy and Security, Data Heterogeneity, Model Aggregation, NonIID Data, Model Convergence, Client Selection, Adaptive Client Selection, Communication EfficiencyAbstract
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across multiple decentralized edge devices or servers while keeping the data localized. This approach not only enhances privacy and security but also leverages the collective power of distributed data to build more robust and accurate models. However, the integration of FL with Big Data analytics presents several challenges, including data heterogeneity, communication efficiency, and model convergence. This paper provides a comprehensive overview of the state-of-the-art in FL for Big Data analytics, highlighting the key challenges and opportunities. We discuss the theoretical foundations, practical implementations, and recent advancements in FL, and propose potential solutions to address the identified challenges. Additionally, we present a case study and a novel algorithm to demonstrate the practical application of FL in a Big Data environment
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