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

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.