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| Apprendimento Federato Regolarizzato× | Apprendimento per trasferimento× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2020 | 2010 (formalized); 1990s (early roots) |
| Ideatore≠ | Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 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 ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Alias | FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Correlati≠ | 6 | 3 |
| 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. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
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