Salīdzināt metodes

	Regularizēta federatīvā apmācība	Regularizētā loģistikā regresija
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2020	1996–2005
Autors	Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base)	Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net)
Tips	Distributed optimization with regularization	Penalized classification model
Pirmavots	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.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Citi nosaukumi	FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization	penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression
Saistītās	6	5
Kopsavilkums	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.	Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces.