Self-Simulation and Meta-Model Aggregation-Based Heterogeneous-Graph-Coupled Federated Learning

Caihong Yan, Xiaofeng Lu, Pietro Lio, Pan Hui, Daojing He

Abstract: A heterogeneous information network (heterogeneous graph) federated learning plays a crucial role in enabling multiparty collaboration in the Internet of Things system. However, due to differences in business and data, the local models of each participant are heterogeneous and unable to achieve federated aggregation. Furthermore, the nonindependent and identically distributed (non-IID) coupling topology structure among participants severely impacts the performance of federated learning. Given the lack of appropriate solutions to these issues, this study proposes a novel heterogeneous graph federated learning framework (HGFL+) based on self-simulation and meta-model aggregation, which includes the following two innovative techniques: 1) the missing coupling supplement module simulates new neighbor nodes on its original heterogeneous graph, and constructs associated edges using multiple encoder-decoder structures, thereby achieving the supplement of missing neighbors with better results than external generative methods and 2) the heterogeneous model aggregation algorithm realizes the fusion of multiparty heterogeneous graph information through mapping, splitting, aggregating, and recombining multiple stages based on the meta-model (the largest basic model unit among participants). We theoretically analyzed the applicability and effectiveness of HGFL+, demonstrating the generalization boundary of HGFL+. Meanwhile, multidimensional empirical verification of classification performance, convergence effect, time overhead, model size, and application extension (model, task, domain) validates the effectiveness of the proposed method.