Сравнение методов

	Регуляризованное федеративное обучение	Федеративное обучение
Область	Машинное обучение	Конфиденциальность
Семейство	Machine learning	Machine learning
Год появления	2020	2017
Автор метода	Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base)	McMahan et al.
Тип	Distributed optimization with regularization	Distributed privacy-preserving machine learning
Основополагающий источник	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.	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.
Другие названия	FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Связанные	6	3
Сводка	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.