Federated Learning (FL) represents a shift in how machine learning models are trained, offering a decentralized approach that prioritizes data privacy. Unlike traditional models that rely on centralized data collection, FL allows multiple devices or organizations to collaboratively train models without sharing their raw data. This approach is particularly valuable in fields like smart health and fintech services, where data privacy and security are paramount.
In the realm of healthcare, FL enables hospitals to use patient data for training models without compromising sensitive information. This can lead to improved diagnostic models and better patient care. Similarly, in the financial sector, FL helps detect fraudulent activities across banks without sharing customer data directly, ensuring privacy while improving the robustness of fraud detection systems.
Federated Learning comes in various forms, such as Horizontal Federated Learning (HFL), which deals with similar datasets, and Vertical Federated Learning (VFL), which combines complementary datasets. It also includes Federated Transfer Learning (FTL), which allows knowledge transfer between different datasets, making it adaptable to a range of applications.
As data privacy becomes increasingly crucial, FL offers a promising solution for secure, collaborative AI. While still evolving, its potential to balance data utility with privacy makes it a key technology for the future of AI-driven industries.
DOI link: https://dx.doi.org/10.61927/igmin112
Full Text: https://www.igminresearch.com/articles/html/igmin112
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