# Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETs

arXiv: https://arxiv.org/abs/2609.36157 · HTML: https://mlsanity.com/paper/2609.36157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Exploiting Shared Representations for Personalized Federated Learning](https://mlsanity.com/paper/2102.07078/) (2021)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Federated Multi-Task Learning under a Mixture of Distributions](https://mlsanity.com/paper/2108.10252/) (2021)
- [Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2006.01412/) (2020)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification](https://mlsanity.com/paper/2206.01748/) (2022)
- [Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs](https://mlsanity.com/paper/2401.10361/) (2024)
- [Analytical Models of the Performance of IEEE 802.11p Vehicle to Vehicle Communications](https://mlsanity.com/paper/2104.07923/) (2021)
- [Multi-task Federated Learning with Encoder-Decoder Structure: Enabling Collaborative Learning Across Different Tasks](https://mlsanity.com/paper/2504.09800/) (2025)

## Abstract (arXiv)

Most federated learning frameworks for vehicular ad hoc networks assume that all vehicles collaboratively train a single model for a common task. This assumption limits their applicability to practical vehicular environments, where vehicles may perform heterogeneous but related perception tasks with different output spaces. This paper proposes encoder-sharing hierarchical multi-task federated learning (EN-HMTFL), which integrates cluster-based hierarchical federated learning with a globally shared encoder and vehicle-local decoders. EN-HMTFL enables vehicles performing different tasks to collaboratively learn a transferable feature representation while preserving their task-specific models locally. Only the encoder is exchanged and aggregated through the hierarchy, whereas raw data and local decoder parameters remain at the vehicles. The proposed framework is evaluated on the MNIST and GTSRB datasets in different vehicular scenarios. Across the evaluated scenarios, EN-HMTFL improves accuracy by up to 24.0% relative to the compared representation-sharing benchmark. In scenarios where EN-HMTFL converges earlier, the reduction reaches up to 69 communication rounds (28.8%).