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This research introduces MOSAIC-FL, a micro-service architecture for Federated Learning that enhances privacy in sensitive domains like genomics through secure aggregation and homomorphic encryption. By integrating fault-tolerant protocols with noise flooding to mitigate key-recovery attacks, the framework enables blind model aggregation while minimizing communication overhead across diverse datasets including breast cancer subtyping. The work advances practical implementations of cryptographic security within distributed machine learning systems for healthcare applications.
MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics
arXiv:2607.25107v1 Announce Type: cross Abstract: 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.
Abstract: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.
8 pages, 4 figures, 2 tables. Accepted and presented at SECRYPT 2026, 23rd International Conference on Security and Cryptography, Porto, Portugal, 16-18 July 2026. this https URL
Cryptography and Security (cs.CR); Machine Learning (cs.LG)
E.3; C.2.4; I.2.11; J.3
arXiv:2607.25107 [cs.CR]
(or arXiv:2607.25107v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2607.25107 arXiv-issued DOI via DataCite (pending registration)
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