Salīdzināt metodes

	Regularizēta federatīvā apmācība	Federatīvā apmācība
Nozare	Mašīnmācīšanās	Privātums
Saime	Machine learning	Machine learning
Izcelsmes gads	2020	2017
Autors	Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base)	McMahan et al.
Tips	Distributed optimization with regularization	Distributed privacy-preserving machine learning
Pirmavots	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.
Citi nosaukumi	FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Saistītās	6	3
Kopsavilkums	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.