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DFed-SST: Building Semantic- and Structure-aware Topologies for Decentralized Federated Graph Learning

arXiv 2508.11530 ↗First posted 15 Aug 2025cs.LGcited by 0

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Abstract (arXiv)

Decentralized Federated Learning (DFL) has emerged as a robust distributed paradigm that circumvents the single-point-of-failure and communication bottleneck risks of centralized architectures. However, a significant challenge arises as existing DFL optimization strategies, primarily designed for tasks such as computer vision, fail to address the unique topological information inherent in the local subgraph. Notably, while Federated Graph Learning (FGL) is tailored for graph data, it is predominantly implemented in a centralized server-client model, failing to leverage the benefits of decentralization.To bridge this gap, we propose DFed-SST, a decentralized federated graph learning framework with adaptive communication. The core of our method is a dual-topology adaptive communication mechanism that leverages the unique topological features of each client's local subgraph to dynamically construct and optimize the inter-client communication topology. This allows our framework to guide model aggregation efficiently in the face of heterogeneity. Extensive experiments on eight real-world datasets consistently demonstrate the superiority of DFed-SST, achieving 3.26% improvement in average accuracy over baseline methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 15 Aug 2025.

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References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  3. 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
  4. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 976
  5. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
  6. 2017Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descentcited by 502
  7. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  8. 2018Stochastic Gradient Push for Distributed Deep Learningcited by 152
  9. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  10. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 117
  11. 2021Federated Graph Classification over Non-IID Graphscited by 114
  12. 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 64
6 more
  1. 2024FedGTA: Topology-aware Averaging for Federated Graph Learningcited by 44
  2. 2022Topology-aware Generalization of Decentralized SGDcited by 38
  3. 2024FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learningcited by 27
  4. 2021Decentralized federated learning of deep neural networks on non-iid datacited by 25
  5. 2024AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneitycited by 25
  6. 2025From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challengescited by 4

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