Federated Learning: Privacy-Preserving Collaborative Machine Learning
Keywords:
federated learning, privacy-preserving, fraud detectionAbstract
Private model training across decentralized data sources using federated learning (FL) transforms collaborative machine learning. Federation-based learning trains models on data from many nodes without raw data interchange. The abstract covers federated learning's basics, architecture, applications, problems, and future research.
Federated learning allows several users to train a global model without sharing data. Initialize and distribute global models to nodes. Nodes only submit gradients and parameters to a central server after learning their dataset. The server sends the nodes the global model for training from these adjustments. Loop continues until model works.
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