FEDERATED AI FRAMEWORKS FOR REGULATED INDUSTRIES: CROSS-DOMAIN INTELLIGENCE FOR SOCIAL SERVICES, INSURANCE, AND INDUSTRIAL OPERATIONS
DOI:
https://doi.org/10.15662/anvej831Keywords:
Federated Learning, Privacy-Preserving Machine Learning, Cross-Domain Intelligence, GDPR, Regulated Industries, Homomorphic Encryption, Differential Privacy, Industrial AI, Social Services Analytics, Insurance AutomationAbstract
The increasing dependence on data-driven decision systems across regulated
sectors—such as social services, insurance, and industrial operations—has amplified
the need for secure collaboration frameworks that preserve privacy while enabling
collective intelligence. Federated Artificial Intelligence (AI) offers a paradigm shift by
allowing models to be trained across distributed data silos without transferring
sensitive information. This paper explores the design, architecture, and implications of
Federated AI Frameworks tailored for highly regulated domains. The proposed
framework integrates federated learning (FL) with privacy-preserving machine
learning (PPML) techniques such as differential privacy, secure aggregation, and
homomorphic encryption to ensure compliance with data protection standards like
GDPR and HIPAA. Through cross-domain case studies, the research demonstrates how
federated learning can enhance fraud detection in social welfare systems, optimize
claim processing in insurance, and improve predictive maintenance in industrial
environments. Comparative evaluations between centralized and federated models
reveal that Federated AI achieves near-equivalent accuracy while drastically reducingdata exposure risks. This study concludes with a roadmap for developing scalable,
regulation-aware federated ecosystems that support ethical and transparent AI in
critical industries.
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