Geometric Fairness-Aware Routing for Federated Edge Networks

• URL: http://arxiv.org/abs/2606.26125v1
• Date: Sun, 31 May 2026 15:02:58 GMT
• Title: Geometric Fairness-Aware Routing for Federated Edge Networks
• Abstract summary: 6G and edge-intelligent networks require effective and balanced routing algorithms.<n>This paper describes Geo-FairFed, a geometric fairness-aware routing system.<n>Geo-FairFed minimizes average latency by 20%, reduces energy consumption by 17%, and improves fairness by up to 21%.
• Score: 3.595796837069228
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Emerging 6G and edge-intelligent networks require effective and balanced routing algorithms among varied and spatially distributed devices. Existing federated routing systems often prioritize aggregate latency or throughput above fairness and the underlying geometric structure of network topologies. This paper describes Geo-FairFed, a geometric fairness-aware routing system that blends hyperbolic graph neural networks (HGNNs) and federated optimization to provide equal performance across edge nodes. Each node learns topology-aware representations on a negatively curved manifold, which include hierarchical relationships and connection asymmetries. A global aggregator next enforces fairness using a curvature-regularized aim that minimizes routing loss, geometric inconsistency, and an inequality penalty based on Jain's fairness index. A theoretical analysis develops convergence guarantees under limited curvature and shows that the proposed fairness term results in a Pareto-improving equilibrium in routing performance. Extensive simulations on dynamic 6G-edge and IoT topologies reveal that Geo-FairFed minimizes average latency by 20\%, reduces energy consumption by 17\%, and improves fairness by up to 21\% when compared to state-of-the-art federated and geometric routing protocols. The study found that embedding topology in a hyperbolic manifold and including fairness into federated updates can significantly enhance the efficiency and equity of large-scale network routing.

Related papers

• GeoRouteNet: Geometry-Enhanced Non-Autoregressive Neural Solver for the Traveling Salesman Problem [5.202950948929751]
  GeoRouteNet is a geometry-enhanced neural solver for Euclidean traveling salesman problem (TSP)<n>On the model side, GeoRouteNet incorporates centered node features, learnable radial distance basis functions, distance-aware graph attention, LayerNorm-SwiGLU feed-forward blocks, and cross-layer attentive residual encoder mixing.<n>On the training side, we design multi-candidate self-comparison reinforcement learning (MCS-RL), which samples multiple candidate tours per instance.
  arXiv  Detail & Related papers  (2026-06-22T02:31:22Z)
• Limit Analysis of Graph Neural Networks with Wireless Conflict Graphs [81.39865232674413]
  Graph Neural Networks (GNNs) have emerged as a powerful tool for wireless resource allocation.<n>This work establishes theoretical results for transferability of GNNs over graphs derived from sparse Random Geometric Graphs (RGGs)
  arXiv  Detail & Related papers  (2026-06-02T15:42:14Z)
• Topology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks [1.7616042687330642]
  We develop a topology-aware Reinforcement Learning architecture based on Twin Delayed Deep Deterministic Policy Gradient (TD3)<n>We investigate three graph neural networks (GNNs) as graph feature encoders for ESS dispatch.<n>Results show that GNN-based controllers consistently reduce the number and magnitude of voltage violations.
  arXiv  Detail & Related papers  (2026-03-27T10:32:18Z)
• Alternating Gradient Flow Utility: A Unified Metric for Structural Pruning and Dynamic Routing in Deep Networks [52.153950303594684]
  We propose a decoupled kinetic paradigm inspired by Alternating Gradient Flow (AGF)<n>AGF accurately captures the network's structural "kinetic utility"<n>We design a hybrid routing framework that decouples AGF-guided offline structural search from online execution via zero-cost physical priors.
  arXiv  Detail & Related papers  (2026-03-12T18:19:21Z)
• Learning Wireless Interference Patterns: Decoupled GNN for Throughput Prediction in Heterogeneous Multi-Hop p-CSMA Networks [4.303580795892996]
  Decoupled Graph Convolutional Network (D-GCN) is a novel architecture that explicitly separates processing of a node's own transmission probability from neighbor interference effects.<n>D-GCN attains 3.3% NMAE, outperforms strong baselines, remains tractable even when exact analytical methods become computationally infeasible.
  arXiv  Detail & Related papers  (2025-10-15T22:13:59Z)
• A Federated Fine-Tuning Paradigm of Foundation Models in Heterogenous Wireless Networks [43.049216349313575]
  Edge devices switch to LoRA modules dynamically for federated fine-tuning with base station to jointly mitigate the impact of heterogeneous transmission.<n>An online optimization algorithm is developed to solve problems with computational complexity.<n>The simulation results on the SST-2 and QNLI data sets demonstrate the performance gains in test accuracy and energy efficiency.
  arXiv  Detail & Related papers  (2025-09-05T06:38:36Z)
• NetDiff: Deep Graph Denoising Diffusion for Ad Hoc Network Topology Generation [1.6768151308423371]
  We introduce NetDiff, a graph denoising diffusion probabilistic architecture that generates wireless ad hoc network link topologies. Our results show that the generated links are realistic, present structural properties similar to the dataset graphs', and require only minor corrections and verification steps to be operational.
  arXiv  Detail & Related papers  (2024-10-09T15:39:49Z)
• Chasing Fairness in Graphs: A GNN Architecture Perspective [73.43111851492593]
  We propose textsfFair textsfMessage textsfPassing (FMP) designed within a unified optimization framework for graph neural networks (GNNs) In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets.
  arXiv  Detail & Related papers  (2023-12-19T18:00:15Z)
• Hierarchical Multi-Marginal Optimal Transport for Network Alignment [52.206006379563306]
  Multi-network alignment is an essential prerequisite for joint learning on multiple networks. We propose a hierarchical multi-marginal optimal transport framework named HOT for multi-network alignment. Our proposed HOT achieves significant improvements over the state-of-the-art in both effectiveness and scalability.
  arXiv  Detail & Related papers  (2023-10-06T02:35:35Z)
• T-GAE: Transferable Graph Autoencoder for Network Alignment [79.89704126746204]
  T-GAE is a graph autoencoder framework that leverages transferability and stability of GNNs to achieve efficient network alignment without retraining. Our experiments demonstrate that T-GAE outperforms the state-of-the-art optimization method and the best GNN approach by up to 38.7% and 50.8%, respectively.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• On Topology Optimization and Routing in Integrated Access and Backhaul Networks: A Genetic Algorithm-based Approach [70.85399600288737]
  We study the problem of topology optimization and routing in IAB networks. We develop efficient genetic algorithm-based schemes for both IAB node placement and non-IAB backhaul link distribution. We discuss the main challenges for enabling mesh-based IAB networks.
  arXiv  Detail & Related papers  (2021-02-14T21:52:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.