পদ্ধতির তুলনা করুন

	রেগুলারাইজড ফেডারেটেড লার্নিং	ফেডারেটেড লার্নিং
ক্ষেত্র	যন্ত্র শিখন	গোপনীয়তা
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2020	2017
প্রবর্তক	Li, T. et al. (FedProx); McMahan, B. et al. (FedAvg base)	McMahan et al.
ধরন	Distributed optimization with regularization	Distributed privacy-preserving machine learning
মৌলিক উৎস	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.
অপর নাম	FedProx, federated learning with regularization, proximal federated learning, penalized federated optimization	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
সম্পর্কিত	6	3
সারসংক্ষেপ	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.