Privacy-Preserving Federated Learning for Reward-Redemption Optimization
Keywords:
federated learning, privacy-preserving AI, reward redemption, encrypted gradients, differential privacyAbstract
The objective of this paper is to describe a federated learning architecture to optimise reward-redemption behaviour in mobile wallet ecosystems at the same time protecting user privacy and complying with national data protection laws. Edge learners within wallet clients utilise wallet balance, transaction history, and other data to predict whether a user would spend a reward.
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