Exploring the Vulnerabilities of Federated Learning: A Deep Dive Into Gradient Inversion Attacks

Summary

This systematic review categorizes Gradient Inversion Attacks (GIA) on Federated Learning (FL) into three types: optimization-based (OP-GIA), generation-based (GEN-GIA), and analytics-based (ANA-GIA). The authors evaluate each type's effectiveness, practicality and detectability. They find OP-GIA the most practical setting despite weak performance, while GEN-GIA and ANA-GIA are impractical due to dependencies and easy detection.

Mitigation

The authors offer a three-stage defense pipeline for users designing FL frameworks and protocols, but the source text does not give its stages.

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