方法对比
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| 正则化联邦学习× | 正则化逻辑回归× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020 | 1996–2005 |
| 提出者≠ | Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base) | Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net) |
| 类型≠ | Distributed optimization with regularization | Penalized classification model |
| 开创性文献≠ | 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 ↗ |
| 别名 | FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization | penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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