FedLLM: A Privacy-Preserving Federated Large Language Model for Explainable Traffic Flow Prediction

• URL: http://arxiv.org/abs/2604.16612v1
• Date: Fri, 17 Apr 2026 18:09:44 GMT
• Title: FedLLM: A Privacy-Preserving Federated Large Language Model for Explainable Traffic Flow Prediction
• Abstract summary: This study proposes FedLLM, a privacy- and distributed framework for explainable multi-horizon short-term traffic flow prediction.<n>FedLLM allows each client to learn from local traffic patterns while contributing to a shared global model.<n> Experimental results show that FedLLM achieves improved predictive performance over centralized baselines.
• Score: 1.2043574473965315
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Traffic prediction plays a central role in intelligent transportation systems (ITS) by supporting real-time decision-making, congestion management, and long-term planning. However, many existing approaches face practical limitations. Most spatio-temporal models are trained on centralized data, rely on numerical representations, and offer limited explainability. Recent Large Language Model (LLM) methods improve reasoning capabilities but typically assume centralized data availability and do not fully capture the distributed and heterogeneous nature of real-world traffic systems. To address these challenges, this study proposes FedLLM (Federated LLM), a privacy-preserving and distributed framework for explainable multi-horizon short-term traffic flow prediction (15-60 minutes). The framework introduces four key contributions: 1) a Composite Selection Score (CSS) for data-driven freeway selection that captures structural diversity across traffic regions 2) a domain-adapted LLM fine-tuned on structured traffic prompts encoding spatial, temporal, and statistical context 3) FedLLM framework, that enables collaborative training across heterogeneous clients while exchanging only lightweight LoRA adapter parameters, 4) a structured prompt representation that supports contextual reasoning and cross-region generalization. The FedLLM design allows each client to learn from local traffic patterns while contributing to a shared global model through efficient parameter exchange, reducing communication overhead and keeping data private. This setup supports learning under non-IID traffic distributions. Experimental results show that FedLLM achieves improved predictive performance over centralized baselines, while producing structured and explainable outputs. These findings highlight the potential of combining FL with domain-adapted LLMs for scalable, privacy-aware, and explainable traffic prediction.

Related papers

• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• Cross-Domain Federated Semantic Communication with Global Representation Alignment and Domain-Aware Aggregation [102.51096131854848]
  This work is the first to consider the domain shift in training the semantic communication system for the image reconstruction task.<n>The proposed approach outperforms the model-contrastive FL (MOON) framework by 0.5 for PSNR values under three domains at an SNR of 1 dB.
  arXiv  Detail & Related papers  (2025-11-30T03:19:59Z)
• Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks [63.541114376141735]
  Large language models (LLMs) are proliferating rapidly at the edge, delivering intelligent capabilities across diverse application scenarios.<n>However, their practical deployment in collaborative scenarios confronts fundamental challenges: privacy vulnerabilities, communication overhead, and computational bottlenecks.<n>We propose Federated Attention (FedAttn), which integrates the federated paradigm into the self-attention mechanism.
  arXiv  Detail & Related papers  (2025-11-04T15:14:58Z)
• Vision-LLMs for Spatiotemporal Traffic Forecasting [14.700408329373998]
  Large Language Models (LLMs) inherently struggle to model the complex spatial dependencies of grid-based traffic data.<n>We propose ST-Vision-LLM, a novel framework reframe thatstemporal forecasting as a vision-language fusion problem.<n>We show that ST-Vision-LLM outperforms existing methods by 15.6% in long-term prediction accuracy and exceeds the second-best baseline by over 30.04% in cross-domain scenarios.
  arXiv  Detail & Related papers  (2025-10-13T11:15:56Z)
• Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions [59.5243730853157]
  Federated learning (FL) provides a privacy-preserving solution for fine-tuning pre-trained large language models (LLMs) using distributed private datasets.<n>This article conducts a comparative analysis of three advanced federated LLM (FedLLM) frameworks that integrate knowledge distillation (KD) and split learning (SL) to mitigate these issues.
  arXiv  Detail & Related papers  (2025-01-08T11:37:06Z)
• Strada-LLM: Graph LLM for traffic prediction [62.2015839597764]
  A considerable challenge in traffic prediction lies in handling the diverse data distributions caused by vastly different traffic conditions.<n>We propose a graph-aware LLM for traffic prediction that considers proximal traffic information.<n>We adopt a lightweight approach for efficient domain adaptation when facing new data distributions in few-shot fashion.
  arXiv  Detail & Related papers  (2024-10-28T09:19:29Z)
• Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation [8.495633193471853]
  Federated Learning has emerged as a promising technique for Traffic Prediction. Current FLTP frameworks lack a real-time model updating scheme. We propose NeighborFL, an individualized real-time federated learning scheme.
  arXiv  Detail & Related papers  (2024-07-17T00:42:47Z)
• Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting [11.253575460227127]
  In this paper, we perform the first study of forecasting traffic flow adopting Online Learning (OL) manner in FL framework. We then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC) to guarantee performance gains regardless of traffic fluctuation.
  arXiv  Detail & Related papers  (2023-02-17T02:37:36Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

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.