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Trading Off Privacy, Utility, and Efficiency in Federated Learning
2023
ACM Transactions on Intelligent Systems and Technology
Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the opposing requirements in preserving privacy and maintaining high model utility . In addition, it is a mandate for a federated learning system to achieve high efficiency in order to enable large-scale model training and deployment. We propose a unified federated learning
doi:10.1145/3595185
fatcat:xqfja4antvgbhl7lmqkbyffolu