Jul 23, 2022 · Federated learning is proposed to ensure that all parties can collaboratively complete the training task while the data is not out of the local.
5 days ago · In this paper, we analyze the current bottlenecks of vertical federated learning under homomorphic encryption comprehensively and numerically.
Jul 21, 2022 · In this paper, we analyze the current bottlenecks of vertical federated learning under homomorphic encryption comprehensively and numerically.
Sep 26, 2024 · The paper proposes to use decoupled forward and backward propagation in vertical federated learning with adversarial attacks. The main idea is ...
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Vertical Federated Learning (VFL) is a solution increasingly used by companies with the same user group but differing features, enabling them to collaboratively ...
Mar 25, 2024 · In this paper, We propose the BHerd strategy which selects a beneficial herd of local gradients to accelerate the convergence of the FL model.
Dec 1, 2020 · This is the presentation of a paper "Accelerating Intra-party Communication in Vertical Federated Learning with RDMA" in Conext Workshop ...
Oct 22, 2024 · We provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects.
[PDF] Towards Communication-efficient Vertical Federated Learning ...
www.vldb.org › vol15 › p2111-fu
In short, the instance weighting mechanism accelerates the convergence under both settings since it restricts the approximation errors incurred by the stale ...
Jun 10, 2024 · Abstract—Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features.