Privacy-Preserving Federated Learning for Critical Infrastructure: A Systematic Review of Security Threat Models, Deployment Patterns, And Governance
Abstract
This study presented a quantitative systematic synthesis of privacy-preserving federated learning research in critical infrastructure contexts by examining how security threat models, privacy mechanisms, deployment architectures, and governance controls were jointly represented and evaluated across the empirical literature. A total of 120 peer-reviewed and high-quality technical studies were coded and analyzed using structured extraction protocols that transformed heterogeneous experimental reports into comparable variables. Descriptive analysis showed that malicious-client and colluding-client threat models dominated the evidence base (approximately 55% and 32%, respectively), while availability-focused threats were reported in fewer than 20% of studies. Passive inference attacks were evaluated more frequently (67.5%) than active attacks (40.8%), with confidentiality emerging as the most frequently targeted security property (65.8%). In terms of privacy mechanisms, secure aggregation (50.8%) and differential privacy (45.0%) were the most prevalent, whereas cryptographic computation approaches (21.7%) and trusted execution techniques (15.8%) appeared less often and were associated with higher reported overhead. Deployment analysis indicated strong dominance of centralized aggregation (70.8%) and cross-silo regimes (60.0%), reflecting institutional coordination patterns typical of critical infrastructure systems. Governance reporting was uneven, with role definitions (55.8%)…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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