← Back to all news
Hugging Face Trending Papers

More Is Better? Exploring Latent Correspondence in Federated Hypergraph Learning

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.

Related stories

arXiv Machine Learning
Sep 7

PACE: Propagation-Aware Collaborative Correction for One-Shot Personalized Federated Graph Learning

PACE introduces a propagation‑aware collaborative correction for one‑shot personalized federated graph learning. Each client sends a rank‑r update and a diagonal sketch of message moments, allowing the server to construct a correction that anchors to the receiver’s local model. A convex negative‑log‑likelihood calibration selects a single coefficient to blend local and external logits, improving accuracy and weighted‑F1 on most datasets while preserving local predictions when the correction is unhelpful.

By Ruizhe Huang, Chengran Li, Xiaochuan Shi
More like this →
arXiv Machine Learning
Aug 24

FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space

FlatLand is a personalized federated learning approach that embeds each client’s graph data into a tailored Lorentz space, leveraging hyperbolic geometry’s negative curvature to model graph structures. The method introduces a parameter decoupling strategy that separates client‑specific heterogeneity (time‑like parameters) from shared knowledge (space‑like parameters), allowing direct aggregation without extra similarity estimation. Experiments on various federated graph learning tasks show that FlatLand outperforms existing methods, especially in low‑dimensional settings.

By Jiahong Liu, Ram Samarth B B, Xinyu Fu, Menglin Yang, Weixi Zhang, Rex Ying, Irwin King
safety
More like this →
arXiv AI
Aug 12

Sheaf-Based Federated Representation Learning

arXiv:2608. 10016v1 Announce Type: cross Abstract: Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing modalities, model architectures, latent dimensionalities, and local learning objectives.

By Gabriele D'Acunto, Enrico Grimaldi, Valeria Avino, Mario Edoardo Pandolfo, Leonardo Di Nino, Sergio Barbarossa, Paolo Di Lorenzo
ragagentsdiffusionsafety
More like this →
arXiv Machine Learning
Jul 20

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

arXiv:2607. 15687v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs.

By Xunkai Li, Guohao Fu, Yuming Ai, Zhengyu Wu, Hongchao Qin, Rong-Hua Li, Guoren Wang
fine-tuningmultimodalsafety
More like this →
arXiv Machine Learning
Oct 3

From Task Mixtures to Specialized Experts

The paper investigates federated learning where each client’s data consists of unknown mixtures of distinct tasks, a scenario termed compound heterogeneity. It shows that when tasks share a common feature geometry, the optimal model for a mixed client is a convex combination of task‑specific models, motivating input‑dependent routing to specialized experts. The authors propose FedSEE, a method that recovers task experts via a convex program and achieves better performance than baselines, reducing negative transfer by 2.9 points overall and 3.7 points for the worst‑served quartile.

By Hojat Allah Salehi, Mehrdad Mahdavi, Andrew Arash Mahyari, M. Hadi Amini
fine-tuning
More like this →
arXiv Machine Learning
Oct 5

HyperFuse: Fast Self-Supervised Node Embeddings for Attributed Hypergraphs

HyperFuse is a new, label‑free pipeline for fast self‑supervised node embeddings on attributed hypergraphs. It computes structural node coordinates via a spectral relaxation of hypergraph modularity, builds multi‑scale feature summaries with utility‑weighted hyperedges, and trains a lightweight encoder for only 100 epochs. In experiments on nine public hypergraphs, HyperFuse achieved 13–179× speed‑ups over baselines and matched or exceeded their accuracy on most downstream tasks.

By Megha P, Harshit Kumar, Srajan Agarwal, Anirban Banerjee, Olaf Wolkenhauer, Saptarshi Bej
rag
More like this →