In this paper, we propose an efficient and privacy-preserving federated deep learning protocol based on stochastic gradient descent method.
Different from traditional deep learning, federated learning does not need to centralize data from multi-party. Clients store datasets locally and train a ...
This paper proposes an efficient and privacy-preserving federated deep learning protocol based on stochastic gradient descent method by integrating the ...
Jun 4, 2019 · We propose a decentralized Fair and Privacy-Preserving Deep Learning (FPPDL) framework to incorporate fairness into federated deep learning models.
Missing: Efficient | Show results with:Efficient
In this paper, we propose an efficient and privacy- preserving federated deep learning protocol based on stochastic gradient descent method by integrating the ...
This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection.
Missing: Deep | Show results with:Deep
Dec 31, 2023 · We propose Lancelot, an innovative and computationally efficient BRFL framework that employs fully homomorphic encryption (FHE) to safeguard against malicious ...
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A decentralized Fair and Privacy-Preserving Deep Learning (FPPDL) framework to incorporate fairness into federated deep learning models, ...
Sep 12, 2024 · This paper proposes a multiplicative double privacy mask algorithm which is convenient for homomorphic addition aggregation.
Sep 12, 2024 · In order to solve the problems of privacy leakage, high computational overhead and high traffic in some federated learning schemes, this paper ...