Symbiosis Rather Than Aggregation: Toward Generalized Federated Learning via Model Symbiosis
Yuange Liu, Yuru Liu, Weishan Zhang, Chaoqun Zheng, Wei Jiang, Daobin Luo, Qiao Qiao, Lingzhao Meng, Hongwei Zhao, Su Yang · IEEE Internet of Things Journal · 2025
Federated learning (FL) faces significant challenges in scenarios with nonindependent and identically distributed (non-IID) data distributions across participating clients. Traditional aggregation-based approaches often struggle with the inherent misalignment between local and global optimization objectives, which leads to gradient divergence and suboptimal generalization performance. This article proposes a novel FL framework that replaces conventional aggregation with a biologically inspired model symbiosis approach called FedSym, which employs a dual-level symbiotic mechanism. Ectosymbiosis performs coarse-grained hierarchical parameter recombinations through random layer-wise model combination, while endosymbiosis enables fine-grained intralayer parameter fusion through weighted averaging, collectively steering model updates toward flatter loss landscapes. Our theoretical analysis demonstrates that FedSym’s convergence rate is$O({}{1}/{T})$under non-IID conditions, which matches the convergence properties of FedAvg. Extensive evaluations across multiple datasets and model architectures show that FedSym achieves substantial improvements over state-of-the-art FL methods, particularly in challenging scenarios with high data heterogeneity, and demonstrates robust performance across varying numbers of participating clients and federation scales.