विधियों की तुलना करें

	फेडरेटेड लर्निंग	सुरक्षित बहु-पक्षीय संगणना
क्षेत्र	गोपनीयता	गोपनीयता
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2017	1982
प्रवर्तक	McMahan et al.	Andrew Yao
प्रकार	Distributed privacy-preserving machine learning	Cryptographic protocol family
मौलिक स्रोत	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.
उपनाम	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme	MPC, Multi-Party Computation, Privacy-Preserving Computation, Güvenli Çok Taraflı Hesaplama
संबंधित	3	3
सारांश	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.