Work

Poster: Stackelberg Game-based Model Partition and Resource Allocation in Split Federated Learning

Year: 2024

Type: conference-paper

Abstract: This paper investigates dynamic model partitioning and resource allocation in split federated learning, aiming to maximize the utility of clients and the Central Server (CS). We first model the interactions between the CS and clients as a Stackelberg game, where the CS acts as the leader to set payment and allocate computation resources, while clients as followers to determine model partitioning strategies. Then, we transform the problem into a bi-level optimization and propose a Nash-Equilibrium-based Stackelberg Algorithm (NESA) to solve it. Finally, the experimental results indicate that a Stackelberg equilibrium exists between the CS and clients, and NESA achieves higher utility and improves accuracy and convergence speed.

Source: ACM Conference on Embedded Networked Sensor Systems (SenSys)

DOI: 10.1145/3666025.3699416

Authors: JiaXin Xiong, Huan Zhou, Kai Jiang, Liang Zhao, Victor C. M. Leung

Institutions: China Three Gorges University, Northwestern Polytechnical University, Wuhan University, Shenzhen University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: National Natural Science Foundation of China

Awards: G1340186914, G3016592842