Machine learningPrivacy-preserving analysis

Federated Learning

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.

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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. link ↗

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Active Learning Federated Learning Bayesian Federated Learning Differential Privacy Ensemble Federated Learning Homomorphic Encryption Online Federated Learning Online Learning Regularized Federated Learning Robust Federated Learning Secure Multi-Party Computation Self-supervised Federated learning Semi-supervised Federated learning

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