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Projection-Free Decentralized Federated Learning with Privacy Guarantees in Complex Systems

Year: 2025

Type: article

Abstract: Constrained decentralized federated learning (DFL) has garnered significant attention for its decentralized approach, primarily tackling distributed constrained optimization problems in complex systems. In complex scenarios, the computational overhead of projecting onto constraint sets can be substantial. Moreover, in distributed data storage, transmitted model parameters or gradients may contain privacy-sensitive data, risking privacy breaches. To tackle these issues, this paper presents a Differentially Private Decentralized Online Projection-Free Optimization (DPDOPFO) federated learning algorithm. DPDOPFO adopts a Frank–Wolfe (FW) methodology, avoiding projection operations in each iteration. It utilizes gradient information to guide updates, reducing computational costs for high-dimensional problems. Integrated with differential privacy techniques, DPDOPFO ensures individual privacy protection. Additionally, it employs gradient tracking techniques, enhancing model performance and robustness. Theoretical analysis demonstrates DPDOPFO’s sublinear convergence and ϵ-differential privacy. Simulation experiments validate its effectiveness in federated learning problems.

Source: Mathematics

DOI: 10.3390/math14010081

Authors: Chen Zhang, Yan Wang, Lin Yuan, Chaopeng Wang, Peishuo Li, Hong Wang

Institutions: Shandong Xiehe University, Nanjing University of Information Science and Technology

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Natural Science Foundation of Shandong Province