방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 정규화된 연합 학습× | 정규화 로지스틱 회귀× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2020 | 1996–2005 |
| 창시자≠ | Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base) | Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net) |
| 유형≠ | Distributed optimization with regularization | Penalized classification model |
| 원전≠ | 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 ↗ | Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| 별칭 | FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization | penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|