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| 정규화된 연합 학습× | 연합 학습× | |
|---|---|---|
| 분야≠ | 머신러닝 | 프라이버시 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2020 | 2017 |
| 창시자≠ | Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base) | McMahan et al. |
| 유형≠ | Distributed optimization with regularization | Distributed privacy-preserving machine learning |
| 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 ↗ | 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 ↗ |
| 별칭 | FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization | Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme |
| 관련≠ | 6 | 3 |
| 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. | 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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