Machine learningMachine learning

Kujifundisha kwa Kujitegemea kwa Njia ya Shirikishi

Kujifundisha kwa Kujitegemea kwa Njia ya Shirikishi huunganisha mafunzo ya shirikishi — ambapo data huwa haiondoki kwenye vifaa vya ndani — na kazi za awali za kujitegemea kama vile kujifundisha kwa kulinganisha au utabiri uliofichwa. Wateja hujifunza uwakilishi wa madhumuni ya jumla kutoka kwa data zao za ndani ambazo hazina lebo na hushiriki tu masasisho ya modeli, si data ghafi, na seva kuu ambayo huyaunganisha kuwa kiongozi mkuu.

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Soma mbinu kamili

Kwa wanachama pekee

Ingia kwa akaunti ya bure ili kusoma sehemu hii.

Ingia

Method map

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

Kujifundisha kwa Kujitegemea kwa Njia ya Shirikishi

Federated Learning Kujifunza kwa Kiasi Kido…Jifunze kwa Kujisimamia Ujifunzaji Nusu-Simamiwa Kujifunza kwa uhamishaji

Vyanzo

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

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Self-supervised Learning in Federated Settings. ScholarGate. https://scholargate.app/sw/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.

Federated LearningFaragha↔ compare
Kujifunza kwa Kiasi Kidogo cha MifanoUjifunzaji wa Mashine↔ compare
Jifunze kwa KujisimamiaUjifunzaji wa Mashine↔ compare
Ujifunzaji Nusu-SimamiwaUjifunzaji wa Mashine↔ compare
Kujifunza kwa uhamishajiUjifunzaji wa Mashine↔ compare

Compare side by side →

Umeona tatizo kwenye ukurasa huu? Ripoti au pendekeza marekebisho →