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-$N$ activ...
| Authors | Paul Largillier, Karl Paygambar, Cédric Gouy-Pailler et al. |
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| Primary category | cs.CR |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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