vertical federated learning
MARS-VFL: AUnified Benchmark for Vertical Federated Learning with Realistic Evaluation
Vertical Federated Learning (VFL) has emerged as a critical privacy-preserving learning paradigm, enabling collaborative model training by leveraging distributed features across clients. However, due to privacy concerns, there are few publicly available real-world datasets for evaluating VFL methods, which poses significant challenges to related research. To bridge this gap, we propose MARS-VFL, a unified benchmark for realistic VFL evaluation.
MARS-VFL: A Unified Benchmark for Vertical Federated Learning with Realistic Evaluation
Vertical Federated Learning (VFL) has emerged as a critical privacy-preserving learning paradigm, enabling collaborative model training by leveraging distributed features across clients. However, due to privacy concerns, there are few publicly available real-world datasets for evaluating VFL methods, which poses significant challenges to related research. To bridge this gap, we propose MARS-VFL, a unified benchmark for realistic VFL evaluation.
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
Federated Learning (FL) is an evolving paradigm that enables multiple parties to collaboratively train models without sharing raw data. Among its variants, Vertical Federated Learning (VFL) is particularly relevant in real-world, cross-organizational collaborations, where distinct features of a shared instance group are contributed by different parties. In these scenarios, parties are often linked using fuzzy identifiers, leading to a common practice termed as . Existing models generally address either multi-party VFL or fuzzy VFL between two parties. Extending these models to practical multi-party fuzzy VFL typically results in significant performance degradation and increased costs for maintaining privacy.
FederatedTransformer: Multi-PartyVertical FederatedLearningonPracticalFuzzilyLinkedData
Federated Learning (FL) is an evolving paradigm that enables multiple parties to collaboratively train models without sharing raw data.
PS VerticalFederatedLearning
In this paper, we focus on a specific federated learning scenario: Vertical Federated Learning(VFL) [5].
VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?
Vertical Federated Learning (VFL), that trains federated models over vertically partitioned data, has emerged as an important learning paradigm. However, existing VFL methods are facing two challenges: (1) scalability when # participants grows to even modest scale and (2) diminishing return w.r.t.