Decentralized Vertical Federated Learning with Feature Sharing Proof ∞ Research

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Briefing

The paper addresses the critical problem of privacy and efficiency in multi-party vertical federated learning (VFL), where multiple institutions collaborate on models without revealing sensitive data. It proposes SecureVFL, a decentralized framework leveraging a permissioned blockchain, a novel Proof of Feature Sharing (PoFS) consensus algorithm, and an efficient (4,2)-sharing Replicated Secret Sharing (RSS) protocol. This foundational breakthrough enables verifiable and lightweight privacy protection, significantly reducing computational and communication overhead while ensuring participant anonymity and accountability, thereby offering a robust architecture for future decentralized AI applications.

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Context

Before this research, existing VFL implementations frequently relied on trusted third parties, introducing centralization vulnerabilities and inefficiencies. Current cryptographic approaches, such as homomorphic encryption and secret sharing, often incurred high computational and communication costs, primarily supporting two-party scenarios, or lacked mechanisms for verifying participant behavior and ensuring identity anonymity. The prevailing theoretical limitation centered on achieving scalable, verifiable, and truly decentralized multi-party VFL without compromising data privacy or efficiency.

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Analysis

SecureVFL’s core mechanism integrates a permissioned blockchain with a novel consensus algorithm and an optimized secret sharing protocol. The Proof of Feature Sharing (PoFS) consensus allows the first participant to reconstruct the aggregated feature sum to generate a transaction block, thereby streamlining the block generation process and enhancing blockchain throughput. This mechanism is complemented by a (4,2)-sharing Replicated Secret Sharing (RSS) protocol, which ensures feature privacy and computational efficiency by primarily using addition operations for feature summation.

Participants interact anonymously via pseudonyms generated by a Trusted Authority, which can be traced for accountability in cases of malicious activity. This design fundamentally differs from previous approaches by decentralizing coordination, drastically reducing overhead, and providing built-in verifiability and fault tolerance, even if half of the participants go offline.

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Parameters

Core Concept ∞ Proof of Feature Sharing (PoFS)
New Protocol ∞ (4,2)-sharing Replicated Secret Sharing (RSS)
System Name ∞ SecureVFL
Key Authors ∞ Mochan Fan, Zhipeng Zhang, Zonghang Li, Gang Sun, Hongfang Yu, Jiawen Kang, Mohsen Guizani
Blockchain Type ∞ Permissioned Blockchain
Privacy Mechanism ∞ Dynamic Pseudonyms
Security Properties ∞ Verifiability, Confidentiality, Identity Privacy-Preserving, Unlinkability, Reliability

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Outlook

This research opens new avenues for scalable and privacy-preserving collaborative AI, particularly in sensitive domains like finance and healthcare. In the next 3-5 years, this theory could unlock real-world applications such as decentralized credit scoring, secure medical diagnostics, and fraud detection systems where multiple entities pool data without exposing raw information. Future research will likely investigate VFL privacy protection schemes suitable for federated training involving any number of parties, further expanding the applicability and robustness of decentralized machine learning architectures.

SecureVFL fundamentally advances the integration of blockchain and federated learning, providing a robust, privacy-preserving, and efficient framework crucial for the future of decentralized AI.

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