পদ্ধতির তুলনা করুন

	ফেডারেটেড লার্নিং	সুরক্ষিত বহু-পক্ষীয় গণনা
ক্ষেত্র	গোপনীয়তা	গোপনীয়তা
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017	1982
প্রবর্তক	McMahan et al.	Andrew Yao
ধরন	Distributed privacy-preserving machine learning	Cryptographic protocol family
মৌলিক উৎস	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.	Yao, A. C. (1982). Protocols for secure computations. 23rd Annual Symposium on Foundations of Computer Science, 160–164.
অপর নাম	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	MPC, Multi-Party Computation, Privacy-Preserving Computation, Güvenli Çok Taraflı Hesaplama
সম্পর্কিত	3	3
সারসংক্ষেপ	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.	Secure Multi-Party Computation (SMPC) is a cryptographic paradigm that enables two or more parties to jointly compute a function over their private inputs without revealing those inputs to one another. Introduced by Andrew Yao in 1982 through his seminal garbled-circuit construction, SMPC provides provable privacy guarantees grounded in computational hardness assumptions. It underpins modern privacy-preserving data analysis, enabling collaborative computation on sensitive datasets in finance, healthcare, and machine learning.