Linganisha mbinu

	Mafunzo yaliyoimarishwa kwa njia ya shirikishi	Uboreshaji wa Gradient Ulioimarishwa
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2020	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
Mwanzilishi	Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base)	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
Aina	Distributed optimization with regularization	Regularized ensemble (additive tree model)
Chanzo asilia	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
Majina mbadala	FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
Zinazohusiana	6	6
Muhtasari	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 gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.