A Multi-Objective Hierarchical Aggregation Optimization Algorithm for Dynamic Network Structures in Federated Learning

Federated learning facilitates big data analysis and applications while safeguarding user privacy, thus emerging as a pivotal paradigm in distributed machine learning. Building on this foundation, multi-objective federated learning (MOFL) — which focuses on the collaborative optimization of multiple objectives such as communication cost and computational efficiency — has become one of the current research hotspots. However, MOFL still suffers from performance degradation in scenarios with heterogeneous data and dynamic network topologies, limiting its practical applicability. To address these challenges, this paper proposes a multi-objective hierarchical aggregation optimization method tailored for dynamic network structures. Specifically, a hierarchical aggregation mechanism is adopted to tackle the dynamic variations in client-side neural network models, which optimizes the training process of MOFL and significantly enhances computational efficiency under dynamic network and heterogeneous data environments. Experimental results verify that the proposed method achieves remarkable performance improvements across different data distributions: it attains an average performance enhancement of 50.73% compared with the NSGA-III algorithm. Furthermore, comprehensive comparative experiments with other state-of-the-art multi-objective optimization algorithms demonstrate its overall superior performance, confirming the scalability of the proposed method in practical scenarios.