Scalability Challenges in Privacy-Preserving Federated Learning

This blog post reviews scalability, data-distribution, client heterogeneity, and data-quality challenges in privacy-preserving federated learning (PPFL), drawing on interviews and findings from UK-US PETs Prize Challenge winners and researchers; it outlines cryptographic performance trade-offs (FHE, MPC), differential privacy limitations (DP-SGD), vertical vs. horizontal partitioning issues, coordination and validation needs, and recent mitigation approaches such as lightweight cryptographic aggregation, secure input validation, and data valuation techniques.