Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Apprendimento Federato Regolarizzato× | Apprendimento semi-supervisionato× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2020 | 1970s–2006 (formalized) |
| Ideatore≠ | Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base) | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Tipo≠ | Distributed optimization with regularization | Learning paradigm |
| Fonte seminale≠ | Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated Optimization in Heterogeneous Networks. Proceedings of Machine Learning and Systems (MLSys), 2, 429–450. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Alias | FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | Regularized federated learning extends the federated learning framework by adding penalty terms to each client's local objective, anchoring local updates closer to the global model. The canonical formulation — FedProx — adds a proximal term that controls how far any single client can drift, improving convergence and stability when client data distributions differ substantially. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
| ScholarGateInsieme di dati ↗ |
|
|