方法对比
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| 正则化联邦学习× | 联邦学习× | |
|---|---|---|
| 领域≠ | 机器学习 | 隐私 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020 | 2017 |
| 提出者≠ | Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base) | McMahan et al. |
| 类型≠ | Distributed optimization with regularization | Distributed privacy-preserving machine learning |
| 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 ↗ | McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282. link ↗ |
| 别名 | FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization | Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme |
| 相关≠ | 6 | 3 |
| 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. | Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model. |
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