Pith. sign in

REVIEW 5 cited by

Federated Graph Learning -- A Position Paper

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2105.11099 v1 pith:AHGCR3YU submitted 2021-05-24 cs.LG cs.DCcs.NI

Huanding Zhang , Tao Shen , Fei Wu , Mingyang Yin , Hongxia Yang , Chao Wu This is my paper
classification cs.LGcs.DCcs.NI
keywords graphchallengesdatadistributedfederatedlearningapplicationsintra-graph
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Graph neural networks (GNN) have been successful in many fields, and derived various researches and applications in real industries. However, in some privacy sensitive scenarios (like finance, healthcare), training a GNN model centrally faces challenges due to the distributed data silos. Federated learning (FL) is a an emerging technique that can collaboratively train a shared model while keeping the data decentralized, which is a rational solution for distributed GNN training. We term it as federated graph learning (FGL). Although FGL has received increasing attention recently, the definition and challenges of FGL is still up in the air. In this position paper, we present a categorization to clarify it. Considering how graph data are distributed among clients, we propose four types of FGL: inter-graph FL, intra-graph FL and graph-structured FL, where intra-graph is further divided into horizontal and vertical FGL. For each type of FGL, we make a detailed discussion about the formulation and applications, and propose some potential challenges.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

    cs.LG 2026-08 conditional novelty 6.0 of 10

    A federated multimodal graph-learning method that uses server-side routing of topology-aware prototypes to align clients across tasks, modalities, and topologies, outperforming baselines on 8 datasets.

  2. Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach

    cs.LG 2026-01 conditional novelty 6.0 of 10

    FedGALA replaces vector-quantized federated graph foundation models with continuous graph-text contrastive alignment plus prompt tuning, claiming up to 14.37% gains over 22 baselines.

  3. A Comprehensive Data-centric Overview of Federated Graph Learning

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A data-centric taxonomy for Federated Graph Learning that classifies 79 studies by data characteristics and data utilization, plus a discussion of integration with pre-trained large models.

  4. Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants

    cs.LG 2025-07 reject novelty 4.0 of 10

    A federated causal discovery algorithm uses aggregated higher-order cumulants to identify source variables recursively and estimate causal strengths in both horizontal and vertical data partitions.

  5. S2FGL: Spatial Spectral Federated Graph Learning

    cs.LG 2025-07 conditional novelty 4.0 of 10

    S2FGL improves subgraph federated graph learning by injecting prototype-based semantic knowledge and aligning local and global spectral projections, gaining about 1 to 2 points of node classification accuracy.

Pith tools