# Joint Routing and Model Pruning for Decentralized Federated Learning in Bandwidth-Constrained Multi-Hop Wireless Networks

arXiv: https://arxiv.org/abs/2603.15188 · HTML: https://mlsanity.com/paper/2603.15188/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 63 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019)
- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2102.04487/) (2021)
- [Decentralized Federated Learning: Balancing Communication and Computing Costs](https://mlsanity.com/paper/2107.12048/) (2021)
- [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021)
- [Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks](https://mlsanity.com/paper/2007.09511/) (2020)
- [Decentralized Federated Learning via SGD over Wireless D2D Networks](https://mlsanity.com/paper/2002.12507/) (2020)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [FedDQ: Communication-Efficient Federated Learning with Descending Quantization](https://mlsanity.com/paper/2110.02291/) (2021)
- [Can Unstructured Pruning Reduce the Depth in Deep Neural Networks?](https://mlsanity.com/paper/2308.06619/) (2023)
- [Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Task Scheduling](https://mlsanity.com/paper/2106.06934/) (2021)
- [Efficient Wireless Federated Learning with Partial Model Aggregation](https://mlsanity.com/paper/2204.09746/) (2022)
- [Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updates](https://mlsanity.com/paper/2302.04851/) (2023)
- [Multi-Source to Multi-Target Decentralized Federated Domain Adaptation](https://mlsanity.com/paper/2304.12422/) (2023)
- [On-Demand Routing for Urban VANETs using Cooperating UAVs](https://mlsanity.com/paper/1908.05905/) (2019)
- [Tram-FL: Routing-based Model Training for Decentralized Federated Learning](https://mlsanity.com/paper/2308.04762/) (2023)
- [Quantized and Asynchronous Federated Learning](https://mlsanity.com/paper/2410.00242/) (2024)
- [CommunityAI: Towards Community-based Federated Learning](https://mlsanity.com/paper/2311.17958/) (2023)
- [Route-and-Aggregate Decentralized Federated Learning Under Communication Errors](https://mlsanity.com/paper/2503.22186/) (2025)

## Abstract (arXiv)

Decentralized federated learning (D-FL) enables privacy-preserving training without a central server, but multi-hop model exchanges and aggregation are often bottlenecked by communication resource constraints. To address this issue, we propose a joint routing-and-pruning framework that optimizes routing paths and pruning rates to maintain communication latency within prescribed limits. We analyze how the sum of model biases across all clients affects the convergence bound of D-FL and formulate an optimization problem that maximizes the model retention rate to minimize these biases under communication constraints. Further analysis reveals that each client's model retention rate is path-dependent, which reduces the original problem to a routing optimization. Leveraging this insight, we develop a routing algorithm that selects latency-efficient transmission paths, allowing more parameters to be delivered within the time budget and thereby improving D-FL convergence. Simulations demonstrate that, compared with unpruned systems, the proposed framework reduces average transmission latency by 27.8% and improves testing accuracy by approximately 12%. Furthermore, relative to standard benchmark routing algorithms, the proposed routing method improves accuracy by roughly 8%.