FedIGL: Federated Invariant Graph Learning for Non-IID Graphs

FedFINFO: A General Full-Informativeness Federated Graph Learning from Open Cross-Domain Data

FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning

Federated learning (FL) enables collaborative learning over decentralized data silos without centralizing raw data. However, heterogeneous local architectures often induce non-aligned representation spaces, making it difficult to transfer global knowledge across silos. Existing paradigms share this knowledge as model parameters, distilled predictions, or class prototypes, yet all encode it in an absolute space that must be aligned across clients. Heterogeneous backbones break this alignment, so the shared knowledge becomes unreliable and misleads local training. We propose FedTopo, a relation-level framework that encodes global knowledge as class relation topology, capturing how classes relate within each client rather than where they lie in feature space. Each client builds its relation topology from local prototypes and uploads it with class statistics. The server then aggregates these relations in a reliability-aware manner that down-weights weakly supported ones, and broadcasts the global topology to clients. The global topology guides local training by emphasizing topology-similar negative classes. Experiments on three datasets under eight heterogeneous backbones show that FedTopo consistently outperforms parameter-, distillation-, and prototype-sharing baselines, with low communication and no inference overhead. Our code is available at https://github.com/Zhaoyang-Ma/FedTopo.

FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space

Federated learning enables privacy-preserving collaborative training, but highly heterogeneous client data remain challenging, especially in graph federated learning where clients possess structurally diverse graphs. Existing personalized federated learning (PFL) methods ignore the intrinsic geometric properties of diverse graph structures. We propose FlatLand, a novel personalized federated learning method that embeds different clients'data in tailored Lorentz space of hyperbolic geometry. Our key insight is that hyperbolic geometry naturally accommodates the intrinsic negative curvature prevalent in real-world graphs, while the time-like dimension in Lorentz space provides a principled way to encode client-specific heterogeneity. We develop a parameter decoupling strategy that separates heterogeneous information (captured in time-like parameters) from common knowledge (preserved in space-like parameters), enabling direct aggregation without requiring client similarity estimation and extra calculation modules. Empirical results on diverse federated graph learning tasks demonstrate that FlatLand achieves superior performance, particularly in low-dimensional settings.

Disagreement-Aware Subgraph Federated Learning via Uncertainty-Guided Local-Global Alignment

Subgraph federated learning (subgraph FL) enables collaborative graph neural network training without sharing raw graph data, but suffers from severe Non-IID distributions and structural fragmentation. In such settings, Non-IID distributions induce pronounced client specialization, where each client excels in a subset of nodes but remains insufficiently trained on others. However, existing similarity-based aggregation and distillation methods fail to balance client specialization and global generalization, often reinforcing dominant local representations and impairing generalization on under-represented nodes, or over-incorporating global knowledge and thereby disrupting well-optimized local specialization. In this paper, we propose FedDUA, a novel disagreement-aware and uncertainty-guided framework for subgraph FL. Specifically, FedDUA first models cross-client semantic disagreement via a lightweight semantic anchor graph and derives adaptive aggregation weights for reliable global federated knowledge. On the client side, FedDUA introduces an uncertainty-aware local-global semantic alignment mechanism that selectively reinforces representations of confident nodes while guiding uncertain nodes with aggregated global knowledge, thereby balancing local specialization and global generalization. Extensive experiments on six real-world datasets demonstrate that FedDUA consistently outperforms the state-of-the-art subgraph FL methods across varying numbers of clients. Further analyses validate the robustness and effectiveness of the proposed disagreement modeling and uncertainty-aware local-global semantic alignment strategies.

Federated Nonlinear Causal Discovery via Divide-and-Conquer Learning

Federated causal discovery aims to learn causal structures from distributed data without sharing raw samples. Existing federated nonlinear methods adopt a monolithic global strategy that optimizes the entire graph simultaneously, suffering from catastrophic error propagation: a single misidentified edge cascades through the global structure, severely degrading accuracy under heterogeneous and limited local data. We propose DC-FNCD (Divide-and-Conquer based Federated Nonlinear Causal Discovery), which decomposes the global problem into independent per-variable neighborhood learning tasks. The core mechanism is a characteristic function-based conditional independence test whose empirical statistics admit exact linear decomposition across clients, enabling lossless federated aggregation without raw data exchange. Local neighborhoods are merged via conflict-aware skeleton construction and oriented through federated additive noise model testing. Extensive experiments demonstrate that DC-FNCD significantly outperforms state-of-the-art federated baselines. The source code is available at https://github.com/Xianjie-Guo/DC-FNCD.

Domain Sensitive Federated Learning with Fisher-Informed Pruning