The paper surveys collaborative learning methods that move beyond traditional Euclidean data to graph-structured data. It reviews foundational principles for Euclidean settings—learning effectiveness, efficiency, and privacy—and then extends the discussion to graph data, presenting a taxonomy of distribution scenarios, statistical heterogeneities, and standardized problem formulations. The survey also outlines open challenges and future research directions in this emerging field.
By R\'emi Bourgerie, \v{S}ar\=unas Girdzijauskas, Viktoria Fodor
The paper investigates why hyperbolic space outperforms Euclidean space for hierarchical graph data. It quantifies Euclidean crowding by showing that fixed‑dimensional Euclidean embeddings of tree‑like graphs inevitably suffer layer‑wise crowding, leading to weaker generalization bounds. The authors then derive a generalization bound that explains hyperbolic geometry’s advantage and introduce EH‑Fusion, a framework that maps Euclidean GNN representations into hyperbolic space and adaptively fuses both geometries based on neighborhood expansion patterns, with experiments confirming its effectiveness.
arXiv:2606.15277v2 Announce Type: replace-cross
Abstract: Graph-based recommendation models effectively capture high-order collaborative signals from user--item interaction graphs. Federated learning...
By Thi Minh Chau Nguyen, Hien Trang Nguyen, Duc Anh Nguyen, Van Ho-Long, Thanh Trung Huynh, Zhao Ren
arXiv:2606. 15277v1 Announce Type: cross Abstract: Graph-based recommender systems are highly effective at extracting collaborative signals from user--item interactions, and federated learning (FL) allows these models to be trained while preserving user privacy.
By Thi Minh Chau Nguyen, Hien Trang Nguyen, Duc Anh Nguyen, Van Ho-Long, Thanh Trung Huynh, Zhao Ren
The paper investigates federated hypergraph learning where local hyperedge releases can correspond to multiple global hypergraph structures, highlighting identifiability and computational challenges. It shows that pairwise assignment of latent correspondences is tractable while multi-client hyperedge matching is generally intractable, and introduces FedASTRA—a method that learns correspondence‑invariant diffusion representations via adaptive heat diffusion and masked latent prediction, communicating only model parameters. Experiments on eight real‑world datasets, including ogbn‑papers100M, demonstrate that FedASTRA achieves state‑of‑the‑art performance‑communication trade‑offs, reaching 50.07 % accuracy and 29.73 % Macro‑F1 while sending only 59.60 MiB per round.
arXiv:2609.06499v1 Announce Type: new
Abstract: One-shot federated graph learning (FGL) requires the server to estimate client contributions from highly compressed information, yet conventional volum...
By Shutong Zheng, Lele Fu, Sheng Huang, Wei Yang Bryan Lim, Chuan Chen
arXiv:2606. 03307v1 Announce Type: cross Abstract: Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning by leveraging large-scale pre-training for cross-domain inference.
By Yifan Jin, Qirui Ji, Bin Qin, Jiangmeng Li, Lixiang Liu, Fuchun Sun, Changwen Zheng
arXiv:2607. 08368v1 Announce Type: new Abstract: With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning.
By Lingyu Qiu, Daniela Annunziata, Stefano Izzo, Fabio Giampaolo, Francesco Piccialli
arXiv:2606. 06397v1 Announce Type: new Abstract: Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlying structure.
By Shuo Wang, Xiangyu Wang, Quanxin Wang, Bailin Wu, Bokui Wang, Shunyang Huang, Boyan Deng, Haonan Liu, Ruiyi Fang, Zhenxiang Xu, Boyu Wang, Zhao Kang
With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates this problem by minimizing communication rounds, existing iterative fine-tuning or knowledge distillation methods still face challenges such as high server-side computational costs and hyperparameter sensitivity.
Geodesic‑SVDD introduces a distributed learning framework for structured tabular data that transforms each client’s task into a one‑class density estimation on a hypersphere, enabling aggregation of local models as probabilistic manifolds in a single communication round. The method employs a Residual Spherical Encoder and a Geodesic Latent Mixup to enforce strict hyperspherical constraints, preventing manifold collapse, and proposes a One‑Shot Product‑of‑Experts protocol for privacy‑preserving aggregation without iterative synchronization. Experiments on five tabular benchmarks and varying label skew levels show that Geodesic‑SVDD achieves the highest mean AUROC among seven federated methods, surpasses federated tree ensembles by up to 21 AUROC points, and its One‑Shot variant matches centralized Deep SVDD under IID and mild skew conditions.
By Andrea Moleri, Christian Intern\`o, Markus Olhofer, Barbara Hammer, Ali Raza
arXiv:2605. 11165v3 Announce Type: replace Abstract: Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution.
By Ben Rachmut, Luise Ge, William Yeoh, Ning Zhang, Yevgeniy Vorobeychik