Papers Vertical Federated Learning
“Vertical Federated Learning” 태그가 달린 논문 195편 · 필터 해제
VeFIA: An Efficient Inference Auditing Framework for Vertical Federated Collaborative Software
Vertical Federated Learning (VFL) is a distributed AI software deployment mechanism for cross-silo collaboration without accessing participants' data. However, existing VFL work lacks a mechanism to audit the execution c…
Federated LearningVertical Federated LearningEvent-Driven Online Vertical Federated Learning
Online learning is more adaptable to real-world scenarios in Vertical Federated Learning (VFL) compared to offline learning. However, integrating online learning into VFL presents challenges due to the unique nature of V…
Federated LearningVertical Federated LearningSynesthesia of Machines (SoM)-Aided Online FDD Precoding via Heterogeneous Multi-Modal Sensing: A Vertical Federated Learning Approach
This paper investigates a heterogeneous multi-vehicle, multi-modal sensing (H-MVMM) aided online precoding problem. The proposed H-MVMM scheme utilizes a vertical federated learning (VFL) framework to minimize pilot sequ…
Federated LearningVertical Federated LearningReliable Vertical Federated Learning in 5G Core Network Architecture
This work proposes a new algorithm to mitigate model generalization loss in Vertical Federated Learning (VFL) operating under client reliability constraints within 5G Core Networks (CNs). Recently studied and endorsed by…
Federated LearningVertical Federated LearningRandom Client Selection on Contrastive Federated Learning for Tabular Data
Vertical Federated Learning (VFL) has revolutionised collaborative machine learning by enabling privacy-preserving model training across multiple parties. However, it remains vulnerable to information leakage during inte…
Federated LearningPrivacy PreservingRepresentation LearningVertical Federated LearningDeep Latent Variable Model based Vertical Federated Learning with Flexible Alignment and Labeling Scenarios
Federated learning (FL) has attracted significant attention for enabling collaborative learning without exposing private data. Among the primary variants of FL, vertical federated learning (VFL) addresses feature-partiti…
Federated LearningVertical Federated LearningICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning
Vertical federated learning (VFL) enables a paradigm for vertically partitioned data across clients to collaboratively train machine learning models. Feature selection (FS) plays a crucial role in Vertical Federated Lear…
feature selectionFederated LearningVertical Federated LearningTree-based Models for Vertical Federated Learning: A Survey
Tree-based models have achieved great success in a wide range of real-world applications due to their effectiveness, robustness, and interpretability, which inspired people to apply them in vertical federated learning (V…
Federated LearningSurveyVertical Federated LearningDPZV: Elevating the Tradeoff between Privacy and Utility in Zeroth-Order Vertical Federated Learning
Vertical Federated Learning (VFL) enables collaborative training with feature-partitioned data, yet remains vulnerable to privacy leakage through gradient transmissions. Standard differential privacy (DP) techniques such…
Federated LearningVertical Federated LearningForgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learning
Privacy concerns in machine learning are heightened by regulations such as the GDPR, which enforces the "right to be forgotten" (RTBF), driving the emergence of machine unlearning as a critical research field. Vertical F…
Federated LearningMachine UnlearningVertical Federated LearningVFL-RPS: Relevant Participant Selection in Vertical Federated Learning
Federated Learning (FL) allows collaboration between different parties, while ensuring that the data across these parties is not shared. However, not every collaboration is helpful in terms of the resulting model perform…
Federated LearningVertical Federated LearningVertical Federated Continual Learning via Evolving Prototype Knowledge
Vertical Federated Learning (VFL) has garnered significant attention as a privacy-preserving machine learning framework for sample-aligned feature federation. However, traditional VFL approaches do not address the challe…
Continual LearningFederated LearningModel OptimizationPrivacy Preserving+1Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly
Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm that enables multiple parties with distinct feature sets to jointly train machine learning models without sharing their raw data. …
Federated LearningPrivacy PreservingVertical Federated LearningVertical Federated Learning for Failure-Cause Identification in Disaggregated Microwave Networks
Machine Learning (ML) has proven to be a promising solution to provide novel scalable and efficient fault management solutions in modern 5G-and-beyond communication networks. In the context of microwave networks, ML-base…
Federated LearningManagementVertical Federated LearningFederated Learning Strategies for Coordinated Beamforming in Multicell ISAC
We propose two cooperative beamforming frameworks based on federated learning (FL) for multi-cell integrated sensing and communications (ISAC) systems. Our objective is to address the following dilemma in multicell ISAC:…
Federated LearningISACVertical Federated LearningUnlearning Clients, Features and Samples in Vertical Federated Learning
Federated Learning (FL) has emerged as a prominent distributed learning paradigm. Within the scope of privacy preservation, information privacy regulations such as GDPR entitle users to request the removal (or unlearning…
Federated LearningInference AttackKnowledge DistillationMembership Inference Attack+1PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy
We present Poisson Binomial Mechanism Vertical Federated Learning (PBM-VFL), a communication-efficient Vertical Federated Learning algorithm with Differential Privacy guarantees. PBM-VFL combines Secure Multi-Party Compu…
Federated LearningVertical Federated LearningUniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration
Cross-hospital collaboration has the potential to address disparities in medical resources across different regions. However, strict privacy regulations prohibit the direct sharing of sensitive patient information betwee…
Federated LearningPrivacy PreservingRepresentation LearningTransfer Learning+1Synesthesia of Machines (SoM)-Aided FDD Precoding with Sensing Heterogeneity: A Vertical Federated Learning Approach
High complexity in precoding design for frequency division duplex systems necessitates streamlined solutions. Guided by Synesthesia of Machines (SoM), this paper introduces a heterogeneous multi-vehicle, multi-modal sens…
Federated LearningVertical Federated LearningCooperative Decentralized Backdoor Attacks on Vertical Federated Learning
Federated learning (FL) is vulnerable to backdoor attacks, where adversaries alter model behavior on target classification labels by embedding triggers into data samples. While these attacks have received considerable at…
Backdoor AttackFederated LearningMetric LearningVertical Federated Learning