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  • Trading Off Privacy, Utility, and Efficiency in Federated Learning
    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
  • Federated Learning with Local Differential Privacy: Trade-Offs Between . . .
    Federated learning (FL) allows to train a massive amount of data privately due to its decentralized structure Stochastic gradient descent (SGD) is commonly use (LDP) of user data in the FL model with SGD The trade-offs between user privacy, global utility, and transmission rate are proved by defining appropriate metrics for FL with LDP
  • FEDERATED LEARNING WITH LOCAL DIFFERENTIAL PRIVACY: TRADE-OFFS . . . - IACR
    siders privacy, utility, and communication jointly This paper focuses on trade-offs between privacy, utility, and transmis-sion rate, where LDP is provided to the FedSGD model by using a Gaussian mechanism In [11], the trade-offs between those three metrics are analyzed for the non-stochastic gra-dient descent algorithm for learning
  • A Unified Learn-to-Distort-Data Framework for Privacy-Utility Trade-off . . .
    The privacy leakage and utility loss metrics provide a quantitative way to measure the trade-off of privacy-utility within federated learning systems The goal of privacy-preserving federated learning is to design mechanisms that minimize the privacy disclosure while keeping the utility cost within an tolerable range
  • Optimizing Federated Learning with Local Differential Privacy: A . . . - SSRN
    However, achieving an optimal balance between data utility and privacy in federated learning poses a significant challenge This paper presents a game-theoretic framework aimed at achieving an optimal trade-off between privacy and utility in federated learning, ensuring users personalized privacy settings and global model performance
  • Trading Off Privacy, Utility and Efficiency in Federated Learning
    The first quantitative trade-off betw een utility and privacy in federated learning w as provided by [ 51 ] The utility loss is evaluated via the performance reduction of the learning model, and
  • Toward the Tradeoffs Between Privacy, Fairness and Utility in Federated . . .
    Federated learning (FL) [MMR+17, KMA+21] is a novel distributed machine learning approach that guarantees user privacy by ensuring that user data does not leave the local area However, FL has been plagued by two ethical issues: privacy and fairness [] So far, most of the research has considered these two issues separately, but the existence of some kind of intrinsic equilibrium between the two
  • Personalized Privacy-Preserving Federated Learning: Optimized Trade-off . . .
    The emerging federated learning (FL) offers a feasible solution for the privacy preservation of users' sensitive data in training artificial intelligence (AI) models Meanwhile, differential privacy (DP) is widely used in FL to ensure that data privacy is not disclosed during model training However, in the practical deployment of DP in FL, a prominent challenge is that most existing FL


















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