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Eneseteadlik föderaalne õppimine

Eneseteadlik föderaalne õppimine ühendab föderaalse treeningu – kus andmed ei lahku kunagi kohalikest seadmetest – eneseteadlike eelülesannetega, nagu kontrastiivne õppimine või maskeeritud ennustamine. Kliendid õpivad üldotstarbelisi representatsioone omaenda märgistamata andmetest ja jagavad keskserveriga ainult mudeli värskendusi, mitte toorandmeid, mis koondab need globaalseks kodeerijaks.

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Loe meetodi täielikku kirjeldust

Ainult liikmetele

Selle osa lugemiseks logi sisse tasuta kontoga.

Logi sisse

Method map

The neighbourhood of related methods — select a node to explore.

Allikad

  1. Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022). link
  2. Federated learning. Wikipedia. link

Kuidas sellele lehele viidata

ScholarGate. (2026, June 3). Self-supervised Learning in Federated Settings. ScholarGate. https://scholargate.app/et/machine-learning/self-supervised-federated-learning

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

Compare side by side
ScholarGateSelf-supervised Federated learning (Self-supervised Learning in Federated Settings). Loetud 2026-06-15 aadressilt https://scholargate.app/et/machine-learning/self-supervised-federated-learning · Andmestik: https://doi.org/10.5281/zenodo.20539026