Methoden vergleichen

	Homomorphe Verschlüsselung	Föderiertes Lernen
Fachgebiet	Datenschutz	Datenschutz
Familie	Machine learning	Machine learning
Entstehungsjahr	2009	2017
Urheber	Craig Gentry	McMahan et al.
Typ	Lattice-based cryptographic scheme	Distributed privacy-preserving machine learning
Wegweisende Quelle	Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. ACM Symposium on Theory of Computing (STOC), 169–178.	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.
Aliasnamen	FHE, Fully Homomorphic Encryption, Leveled Homomorphic Encryption, Homomorfik Şifreleme	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Verwandt	3	3
Zusammenfassung	Homomorphic Encryption (HE) is a cryptographic framework that allows arbitrary computations to be performed directly on encrypted data without requiring decryption. First realized as a fully general construction by Craig Gentry in 2009 using ideal lattices, it enables a server to process sensitive data and return an encrypted result that, when decrypted by the data owner, equals the result of performing the same computation on the plaintext. It is foundational to privacy-preserving machine learning, secure cloud computing, and confidential analytics.	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.