1、Securing Federated Learning withPrivacy Preserving TechniquesHow Differential Privacy and Homomorphic EncryptionEnable Compliant AIJanuary 7,2026White PaperTable of ContentsExecutive Summary.Overview.Understanding Federated Learning Vulnerabilities.Differential Privacy as a Mathematical Privacy Guar

2、antee.Homomorphic Encryption for Secure Computation.Building a Combined Defense Strategy.Implementation Roadmap for Enterprises.Real-World Applications Across Industries.234681013151shakudo.ioExecutive SummaryAs enterprises race to deploy AI systems,privacy regulations are tightening globally.Gartne

3、r predicts 75%of the worlds population will have personal data protected under privacy laws by 2026,forcing organizations to rethink how they train machine learning models.Federated Learning has emerged as a breakthrough approach,enabling collaborative model training across distributed data sources

4、without centralizing sensitive information.However,FL alone is not inherently securerecent research reveals vulnerabilities to gradient leakage,membership inference,and model poisoning attacks that can compromise privacy guarantees.This whitepaper examines how two complementary techniquesDifferentia

5、l Privacy and Homomorphic Encryptiontransform Federated Learning from a privacy-conscious framework into a truly secure AI training methodology.Differential Privacy provides mathematical guarantees that individual data contributions remain protected even when model updates are shared,while Homomorph

6、ic Encryption enables computations on encrypted data,ensuring updates remain unreadable during transmission and aggregation.For enterprises in regulated industries like healthcare,finance,and government,these techniques enable AI innovation while maintaining data sovereignty,meeting compliance requi