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Enhancing Cross-Problem Vehicle Routing via Federated Learning

arXiv 2604.10652 ↗First posted 12 Apr 2026cs.AI cs.LGcited by 0

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Abstract (arXiv)

Vehicle routing problems (VRPs) constitute a core optimization challenge in modern logistics and supply chain management. The recent neural combinatorial optimization (NCO) has demonstrated superior efficiency over some traditional algorithms. While serving as a primary NCO approach for solving general VRPs, current cross-problem learning paradigms are still subject to performance degradation and generalizability decay, when transferring from simple VRP variants to those involving different and complex constraints. To strengthen the paradigms, this paper offers an innovative "Multi-problem Pre-train, then Single-problem Fine-tune" framework with Federated Learning (MPSF-FL). This framework exploits the common knowledge of a federated global model to foster efficient cross-problem knowledge sharing and transfer among local models for single-problem fine-tuning. In this way, local models effectively retain common VRP knowledge from up-to-date global model, while being efficiently adapted to downstream VRPs with heterogeneous complex constraints. Experimental results demonstrate that our framework not only enhances the performance in diverse VRPs, but also improves the generalizability in unseen problems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 34 of its 44 references that are in the map. First posted on arXiv 12 Apr 2026.

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References · 34

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2022Editing Models with Task Arithmeticcited by 1,076
  4. 2015Pointer Networkscited by 882
  5. 2023TIES-Merging: Resolving Interference When Merging Modelscited by 795
  6. 2023Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunchcited by 679
  7. 2017Federated Multi-Task Learningcited by 669
  8. 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 600
  9. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  10. 2020POMO: Policy Optimization with Multiple Optima for Reinforcement Learningcited by 341
  11. 2018Reinforcement Learning for Solving the Vehicle Routing Problemcited by 327
  12. 2024FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptationscited by 144
22 more
  1. 2023Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalizationcited by 140
  2. 2020Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhoodcited by 122
  3. 2022Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimizationcited by 118
  4. 2021Learning to Delegate for Large-scale Vehicle Routingcited by 108
  5. 2021Matrix Encoding Networks for Neural Combinatorial Optimizationcited by 89
  6. 2024MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Expertscited by 69
  7. 2023Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policycited by 61
  8. 2022Winner Takes It All: Training Performant RL Populations for Combinatorial Optimizationcited by 57
  9. 2024RouteFinder: Towards Foundation Models for Vehicle Routing Problemscited by 54
  10. 2023PyVRP: a high-performance VRP solver packagecited by 50
  11. 2024Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalizationcited by 49
  12. 2025Rethinking Light Decoder-based Solvers for Vehicle Routing Problemscited by 36
  13. 2024PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimizationcited by 35
  14. 2024Learning to Handle Complex Constraints for Vehicle Routing Problemscited by 29
  15. 2024Cross-Problem Learning for Solving Vehicle Routing Problemscited by 24
  16. 2024CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attentioncited by 23
  17. 2022FLIS: Clustered Federated Learning via Inference Similarity for Non-IID Data Distributioncited by 22
  18. 2025BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimizationcited by 14
  19. 2025SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchycited by 8
  20. 2025FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edgecited by 7
  21. 2025Multi-Task Vehicle Routing Solver via Mixture of Specialized Experts under State-Decomposable MDPcited by 7
  22. 2023Federated cINN Clustering for Accurate Clustered Federated Learningcited by 4

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