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Federated learning framework aids aging clock prediction with trust networks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel federated learning framework called TNFL to address challenges in predicting biological aging from distributed molecular datasets. This framework uses a trust network to propagate models between medical centers without centralizing data, mitigating issues like limited local data, sparse trust, and model drift. Experiments demonstrate TNFL's effectiveness in aging clock prediction, its ability to provide interpretable age-dependent patterns, and its success in identifying coherent higher-order biological organizations associated with aging. AI

IMPACT This framework could enable more accurate and privacy-preserving biological aging predictions by leveraging distributed data.

RANK_REASON The cluster contains an academic paper detailing a new framework for machine learning research. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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Federated learning framework aids aging clock prediction with trust networks

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The cluster contains an academic paper detailing a new framework for machine learning research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chunxu Zhang, Bo Li, Wenliang Wang, Yang Liu, Di Jiang, Yuan Huang, Yo-ichi Nabeshima, Akinori Yamamura, Bo Yang, Qiang Yang ·

    A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction

    arXiv:2609.10108v1 Announce Type: cross Abstract: 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 requir…

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