Federated Machine Learning

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Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.

• A Multi-Layer Auditable Vertical Federated Learning Prototype for Power Equipment Supply Chains: Reproducibility, Robustness, and Privacy-Boundary Evaluation

  IoT 2026 · 0 citations

• Friend-Safe Adversarial Attack for selective evasion in personalized federated learning

  Discover Computing 2026 · 0 citations