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Pembelajaran Federasi Mandiri (Self-supervised Federated Learning)

Pembelajaran Federasi Mandiri menggabungkan pelatihan federasi — di mana data tidak pernah meninggalkan perangkat lokal — dengan tugas-tugas pretext mandiri seperti pembelajaran kontrastif atau prediksi bertopeng. Klien mempelajari representasi tujuan umum dari data tak berlabel mereka sendiri dan hanya berbagi pembaruan model, bukan data mentah, dengan server pusat yang mengagregasinya menjadi encoder 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 menyitasi halaman ini

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