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Pembelajaran Bersekutu Kendiri-Selia

Pembelajaran Bersekutu Kendiri-Selia menggabungkan latihan bersekutu — di mana data tidak pernah meninggalkan peranti tempatan — dengan tugasan awal kendiri-selia seperti pembelajaran kontrastif atau ramalan bertopeng. Klien mempelajari perwakilan tujuan umum daripada data mereka sendiri yang tidak berlabel dan hanya berkongsi kemas kini model, bukan data mentah, dengan pelayan pusat yang menggabungkannya menjadi pengekod global.

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Method map

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

Sumber

  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

Cara memetik halaman ini

ScholarGate. (2026, June 3). Self-supervised Learning in Federated Settings. ScholarGate. https://scholargate.app/ms/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). Dicapai 2026-06-15 daripada https://scholargate.app/ms/machine-learning/self-supervised-federated-learning · Set data: https://doi.org/10.5281/zenodo.20539026