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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a preprint describing TNFL, a federated learning framework designed to predict biological aging from distributed molecular datasets while preserving privacy and interpretability. The work addresses a methodological problem (federated learning under privacy constraints) rather than clinical validation, and reports computational analyses of protein interactions without independent clinical outcome data or comparison to established aging clocks.
Computational methods development and retrospective molecular dataset analysis. Molecular datasets distributed across multiple medical centers with privacy constraints; specific eligibility criteria, participant demographics, and number of centers not stated.. Intervention: TNFL federated learning framework with age-aware mixture-of-experts and generative replay components.
TNFL enables aging-clock prediction with limited local data across multiple molecular datasets Framework identifies pairwise protein interactions that form coordinated higher-order subnetworks spanning multiple aging-related biological systems Several proteins recur across identified subnetworks, suggesting coherent higher-order biological organization
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This work presents a technical framework for federated learning of aging biomarkers but does not establish clinical utility or validate performance against established aging clocks. Clinicians and researchers should view this as an early-stage methodological contribution requiring independent validation before use in clinical applications.
A methodological preprint demonstrating a federated learning framework for aging prediction in silico across distributed datasets, not yet peer-reviewed, with no clinical validation or comparison to established aging clocks.
As stated by the source record.
This work presents a technical framework for federated learning of aging biomarkers but does not establish clinical utility or validate performance against established aging clocks. Clinicians and researchers should view this as an early-stage methodological contribution requiring independent validation before use in clinical applications.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks, and are they zeroth-order or higher-order? Addressing these questions requires learning from large molecular datasets distributed across medical centers, where privacy constraints prevent centralized data sharing. Federated learning offers a natural solution but faces four challenges in this setting: limited local sample sizes, sparse and directional inter-center trust, the need to retain discriminative age prediction while supporting interpretation, and model drift and forgetting under heterogeneous cross-center data. We propose TNFL, a trust-network-based federated learning framework that progressively propagates models along directed pairwise trust relations without centralized aggregation. TNFL combines an age-aware mixture-of-experts model with generative replay to preserve previously learned information and reduce forgetting and drift. Experiments across multiple molecular datasets show that TNFL enables effective aging-clock prediction with limited local data, provides interpretable age-dependent prediction patterns, and maintains stable performance across interaction orders. To investigate the biological questions, we analyze TNFL-identified pairwise protein interactions and their higher-order organization through functional and network analyses. The identified interactions repeatedly form coordinated higher-order subnetworks spanning multiple aging-related biological systems, with several proteins recurring across subnetworks. These findings suggest that TNFL captures molecular relationships beyond isolated pairwise associations and reveals coherent higher-order biological organization associated with aging.
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