Federated Learning in Cloud-Based Financial Applications: A Decentralized Approach to AI Training
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I4P101Keywords:
Federated Learning, Machine Learning, Data Privacy, Financial Applications, Decentralized Learning, Security, Communication Efficiency, Data Heterogeneity, Regulatory Compliance, ScalabilityAbstract
Federated Learning (FL) has emerged as a promising paradigm for training machine learning models in a decentralized manner, particularly in cloud-based financial applications. This paper explores the application of FL in the financial sector, highlighting its potential to enhance data privacy, security, and model performance. We begin by providing an overview of FL and its key components, followed by a detailed discussion of the challenges and opportunities in the financial domain. We then present case studies and empirical evaluations to demonstrate the effectiveness of FL in various financial applications, such as fraud detection, credit scoring, and algorithmic trading. Finally, we discuss the future directions and open research questions in this field
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