Catatan bukti metode

Online Federated Learning

Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw observations, and contribute compressed updates to a central server that aggregates them in near-real-time.

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Online Federated Learning (Sequential Distributed Learning without Centralised Data)

Catatan metode taksonomi · ml-model / machine-learning

Buka metode lengkap

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Same method familyDifferential Privacymachine-suggested · Relational suggestion, not evidence.Same method familyFederated Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketOnline Learningmachine-suggested · Relational suggestion, not evidence.Same method familyStochastic Gradient Descentmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketTransfer Learningmachine-suggested · Relational suggestion, not evidence.