MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics

Security and privacy are primordial requirements for Federated Learning (FL), especially in fields such as healthcare and genomics where sensitive information has to be analyzed. Our FL framework is designed to address these challenges while proposing a modular, flexible and micro-service architecture. More precisely, it integrates an efficient gRPC communication layer and a Finite State Machine to ensure robust component synchronization and threat detection, while relying on a fault-tolerant secure aggregation protocol using a Threshold variant of the CKKS homomorphic cryptosystem. This allows blind model aggregation by an orchestration server, requiring a minimum of $t$-out-of-$N$ active clients for decryption while minimizing communication overhead thanks to both cryptographic and network protocols. We ensure IND-CPA-D security through noise flooding and mitigate the recent key-recovery attack on synchronized decryptors by renewing the collective key material at every round. We demonstrate the framework's effectiveness through diverse use cases, ranging from standard image recognition (EMNIST) to complex genomic classification including breast cancer subtyping on TCGA, evaluating system performance across different threshold values and model scales.

Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling

Protecting sensitive health data while enabling collaborative analysis is a central challenge in healthcare. Traditional machine learning (ML) requires institutions to pool anonymized patient records, centralizing analytical development and privacy risks at a single site. Privacy-enhancing technologies (PETs), including Differential Privacy (DP) and Homomorphic Encryption (HE), can mitigate these risks. However, they are mainly studied in conventional data-sharing settings and often introduce trade-offs, including reduced model utility, higher computational cost, and increased implementation complexity. Federated Learning (FL) reduces data centralization by enabling institutions to train models locally and share only model updates. Nevertheless, FL does not eliminate privacy risks, as shared parameters or gradients may still reveal sensitive information. Integrating DP or HE into FL can strengthen privacy guarantees, yet their comparative performance and deployment implications in real-world healthcare settings remain unclear. We systematically evaluated DP and HE integration in FL under real-world conditions, comparing them with standard FL and centralized ML (cML) to quantify privacy-utility trade-offs in multi-institutional settings. Using nationwide Swedish healthcare data, we evaluated cardiovascular disease risk prediction using logistic regression (LR) and neural network (NN) learners. FL with HE achieved performance comparable to cML but introduced measurable cryptographic overhead, particularly in the NN implementation. FL with DP incurred lower computational cost; however, LR was more sensitive to calibrated noise than the NN, resulting in greater performance degradation. Our findings provide practical guidance for deploying privacy-preserving FL in fragmented healthcare systems.