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• The INFL method achieves strong, controllable privacy while maintaining utility across diverse biomedical omics tasks.result 90%
• INFL integrates plug-and-play, coordinate-conditioned modules into client models and embeds a secret key directly into the architecture.method 95%
• INFL's key-conditioned mechanism disrupts gradient-based attacks in federated training.technical 85%
• Existing privacy-preserving federated learning methods for biomedical data suffer from significant computational overhead or performance degradation.limitation 80%
• INFL accommodates biological heterogeneity without task-specific tuning across spatial and non-spatial omics.method 85%
• The model interpretation for the bulk proteomics task identified LGALS4 protein as positively correlated with Adenocarcinoma, consistent with existing literature.result 90%
• Most federated learning studies with privacy methods lack systematic evaluation on noisy, heterogeneous real-world biomedical data, limiting practical adoption.limitation 80%

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