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The article explores the convergence of federated learning and Java-based enterprise applications deployed on AWS Lambda.It introduces a framework that enables decentralized model training across data partitions while maintaining privacy compliance using AWS S3, IAM, and encrypted payload routing via SQS and EventBridge.The architecture extends J2EE legacy systems through Spring Boot APIs that participate in model aggregation without data leakage.A layered defense mechanism using API Gateway, WAF, and IAM roles is evaluated.This approach is validated through a prototype in healthcare analytics to securely infer patient risk scores without centralizing PHI.
Chandra Sekhar Oleti (2022) studied this question.