# Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completion

arXiv: https://arxiv.org/abs/2408.05160 · HTML: https://mlsanity.com/paper/2408.05160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Builds on (most cited first, 23 of 23)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Multi-scale Attributed Node Embedding](https://mlsanity.com/paper/1909.13021/) (2019)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [LDP-Fed: Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2006.03637/) (2020)
- [Subgraph Federated Learning with Missing Neighbor Generation](https://mlsanity.com/paper/2106.13430/) (2021)
- [You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks](https://mlsanity.com/paper/2106.13264/) (2021)
- [HNHN: Hypergraph Networks with Hyperedge Neurons](https://mlsanity.com/paper/2006.12278/) (2020)
- [Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation](https://mlsanity.com/paper/2012.06852/) (2020)
- [HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs](https://mlsanity.com/paper/2010.04558/) (2020)
- [Federated Learning with Local Differential Privacy: Trade-offs between Privacy, Utility, and Communication](https://mlsanity.com/paper/2102.04737/) (2021)
- [Differentially Private Federated Learning with Local Regularization and Sparsification](https://mlsanity.com/paper/2203.03106/) (2022)
- [Federated Graph Learning -- A Position Paper](https://mlsanity.com/paper/2105.11099/) (2021)
- [FedGraph: Federated Graph Learning with Intelligent Sampling](https://mlsanity.com/paper/2111.01370/) (2021)
- [Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy](https://mlsanity.com/paper/1606.05053/) (2016)
- [FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks](https://mlsanity.com/paper/2201.12433/) (2022)
- [Federated Learning over Coupled Graphs](https://mlsanity.com/paper/2301.11099/) (2023)
- [HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN](https://mlsanity.com/paper/2304.10013/) (2023)

## Abstract (arXiv)

The rapid growth of graph-structured data necessitates partitioning and distributed storage across decentralized systems, driving the emergence of federated graph learning to collaboratively train Graph Neural Networks (GNNs) without compromising privacy. However, current methods exhibit limited performance when handling hypergraphs, which inherently represent complex high-order relationships beyond pairwise connections. Partitioning hypergraph structures across federated subsystems amplifies structural complexity, hindering high-order information mining and compromising local information integrity. To bridge the gap between hypergraph learning and federated systems, we develop FedHGL, a first-of-its-kind framework for federated hypergraph learning on disjoint and privacy-constrained hypergraph partitions. Beyond collaboratively training a comprehensive hypergraph neural network across multiple clients, FedHGL introduces a pre-propagation hyperedge completion mechanism to preserve high-order structural integrity within each client. This procedure leverages the federated central server to perform cross-client hypergraph convolution without exposing internal topological information, effectively mitigating the high-order information loss induced by subgraph partitioning. Furthermore, by incorporating two kinds of local differential privacy (LDP) mechanisms, we provide formal privacy guarantees for this process, ensuring that sensitive node features remain protected against inference attacks from potentially malicious servers or clients. Experimental results on seven real-world datasets confirm the effectiveness of our approach and demonstrate its performance advantages over traditional federated graph learning methods.