Compară metode

	Învățare federată regularizată	Învățare prin transfer
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2020	2010 (formalized); 1990s (early roots)
Autorul original	Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Tip	Distributed optimization with regularization	Learning paradigm
Sursa seminală	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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Denumiri alternative	FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Înrudite	6	3
Rezumat	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.