DPZV: Resource Efficient ZO Optimization For Differentially Private VFL

• URL: http://arxiv.org/abs/2502.20565v1
• Date: Thu, 27 Feb 2025 22:07:16 GMT
• Title: DPZV: Resource Efficient ZO Optimization For Differentially Private VFL
• Authors: Jianing Zhang, Evan Chen, Chaoyue Liu, Christopher G. Brinton,
• Abstract summary: Vertical Federated Learning (VFL) enables collaborative model computation across feature-partitioned data, yet faces privacy risks when scaling to large models.<n>We propose DPZV, a memory- Zeroth-efficient(DP) framework that integrates differential privacy.<n>Our framework eliminates backpagation through two-point estimation, reducing client memory usage by 90% compared to first-order counterparts.
• Score: 9.302691218735406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vertical Federated Learning (VFL) enables collaborative model training across feature-partitioned data, yet faces significant privacy risks and inefficiencies when scaling to large models. We propose DPZV, a memory-efficient Zeroth-Order(ZO) optimization framework that integrates differential privacy (DP) with vertical federated learning, addressing three critical challenges: (1) privacy vulnerabilities from gradient leakage, (2) high computation/communication costs of first-order methods, and (3) excessive memory footprint in conventional zeroth-order approaches. Our framework eliminates backpropagation through two-point gradient estimation, reducing client memory usage by 90\% compared to first-order counterparts while enabling asynchronous communication. By strategically injecting Gaussian noise on the server, DPZV achieves rigorous $(\epsilon, \delta)$-DP guarantees without third-party trust assumptions. Theoretical analysis establishes a convergence rate matching centralized case under non-convex objectives. Extensive experiments on image and NLP benchmarks demonstrate that DPZV outperforms all baselines in accuracy while providing strong privacy assurances ($\epsilon \leq 10$) and requiring far fewer computation resources, establishing new state-of-the-art privacy-utility tradeoffs for resource-constrained VFL deployments.

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