手法を比較

	Homomorphic Encryption	Federated Learning（連合学習）
分野	プライバシー	プライバシー
系統	Machine learning	Machine learning
提唱年	2009	2017
提唱者	Craig Gentry	McMahan et al.
種類	Lattice-based cryptographic scheme	Distributed privacy-preserving machine learning
原典	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.
別名	FHE, Fully Homomorphic Encryption, Leveled Homomorphic Encryption, Homomorfik Şifreleme	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
関連	3	3
概要	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.