Title: A Privacy-Preserving and Unified Federated Learning Framework for Trajectory Data Preparation
Venue: NeurIPS 2025
Accepted: no
Keywords: Trajectory Data Preparation

Federated Learning

Large Language Model
Abstract: Trajectory data, which captures the movement patterns of people and vehicles over time and space, is crucial for applications such as traffic optimization and urban planning. However, issues such as noise and incompleteness often compromise data quality, leading to inaccurate trajectory analyses and limiting the potential of these applications. While Trajectory Data Preparation (TDP) can enhance data quality, existing methods suffer from two key limitations: (i) they do not address data privacy concerns, particularly in federated settings where trajectory data sharing is prohibited, and (ii) they typically design task-specific models that lack generalizability across diverse TDP scenarios. To overcome these challenges, we propose FedTDP, a privacy-preserving and unified framework that leverages the multi-task learning capabilities of Large Language Models (LLMs) for TDP in federated environments. Specifically, we: (i) design a trajectory privacy autoencoder for secure data transmission to protect data privacy with theoretical analysis, (ii) introduce a trajectory knowledge enhancer to develop TDP-oriented LLMs by improving model learning of TDP knowledge, and (iii) propose federated parallel optimization to enhance training efficiency by reducing data transmission and enabling parallel model training. Experiments on 6 real datasets and 10 mainstream TDP tasks demonstrate that FedTDP consistently outperforms 13 state-of-the-art baselines. All code and data are available at https://anonymous.4open.science/r/FedTDP.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FedTDP, a privacy-preserving and unified federated learning framework for trajectory data preparation, which effectively combines the strengths of SLMs and LLMs. The framework is designed to address two key challenges in real-world trajectory data applications: the protection of user privacy in decentralized environments and the need for accurate, efficient trajectory data processing. It introduces the Trajectory Privacy Autoencoder to ensure that sensitive user location information is protected during training, and the Trajectory Knowledge Enhancer to improve the model’s understanding of spatio-temporal dependencies through prompt-based knowledge transfer, LoRA-based parameter-efficient tuning, and bidirectional knowledge distillation between SLMs and LLMs.\nThe contributions of this work are as follows: (1) it is the first framework that integrates federated learning with large language models for trajectory data preparation; (2) it proposes a privacy-preserving autoencoder that supports global training without sharing raw trajectory data; (3) it introduces a novel trajectory-specific prompting and tuning mechanism to enhance LLM adaptability to TDP tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nS1.The paper is generally well-written and logically structured, with explanations of core modules that facilitate understanding, although some parts may be difficult for first-time readers.\n\nS2. It introduces a novel problem setting by defining and studying federated trajectory data preparation (F-TDP) for the first time, showing potential impact through its integration of federated learning with trajectory data preparation.\n\nS3. The framework demonstrates notable originality by combining federated learning, prompt-based LLM fine-tuning, and privacy-preserving encoders for trajectory data, using bidirectional knowledge distillation between SLMs and LLMs along with LoRA-based sparse tuning in a split-learning setting, which has not been explored before.\n\nWeaknesses:\n\nW1.Figure 2 lacks clarity in illustrating the interaction between local and cross-client TDP workflows, and the depiction of split learning and alternating optimization could be more intuitive, suggesting the need to visually separate the two pipelines and improve step labeling for readability.\n\nW2.Although F-TDP is proposed as a novel task, its necessity and motivation could be more strongly justified since it is unclear why this specific formulation is essential or how it fundamentally differs from traditional federated learning tasks, and related work does not discuss prior FL applications to TDP while baseline comparisons are imbalanced due to the lack of privacy-preserving designs.\n\nW3.While the integration is innovative, the individual components are known in literature and the paper could strengthen its originality by elaborating why this specific combination is essential for F-TDP and clarifying its distinctiveness, perhaps as a vertical federated problem.\n\nQUESTIONS:\nQ1:In the Trajectory Knowledge Enhancer module, the authors mention that \"Information\" (e.g., road network, weather) is an optional context to enhance model performance on TDP tasks. Could the authors clarify how the model determines whether to include this additional context and which type of information to use for a given task? Is this decision task-specific, manually configured, or automatically inferred during prompt construction? It would be helpful to provide either a decision rule, algorithmic selection process, or ablation comparison showing the impact of using different types of Information (or none).\n\nQ2: The Trajectory Offsite-Tuning section introduces an adapter-based strategy where the adapter A is trained and aggregated.What is the specific relationship between adapter A and the N layers of the base model? Are adapters inserted in all N layers but only a subset AAA of them are aggregated, or are only A layers fine-tuned and aggregated?\nAdditionally, how is the adapter structure shared across different base models, especially across families of SLMs and LLMs? Providing more details on the layer-wise configuration and compatibility requirements for adapter sharing across model families would clarify scalability and extensibility.\n\nLIMITATIONS:\nSee weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FedTDP, a novel framework for trajectory data preparation (TDP) in federated settings using LLMs. The framework introduces three main components:\n- Trajectory Privacy AutoEncoder (TPA) to encode spatio-temporal data and protect privacy.\n- Trajectory Knowledge Enhancer (TKE) for adapting LLMs/SLMs to TDP tasks via prompt engineering, offsite tuning, and LoRA sparse-tuning.\n- Federated Parallel Optimization (FPO) to reduce training overhead using split learning and alternating optimization.\nThe authors evaluate FedTDP across 10 TDP tasks and 6 real-world datasets (seen/unseen), showing substantial improvements over non-LLM, table-based LLM, and spatio-temporal LLM baselines. Ablation studies and efficiency experiments support the value of each component.\n\nSTRENGTHS AND WEAKNESSES:\n1. Proposes Federated Trajectory Data Preparation (F-TDP), a timely and relevant problem that merges federated learning, trajectory modeling, and LLM adaptation.\n\n2. Well-defined components (TPA, TKE, FPO) address core challenges: privacy, TDP adaptation, and efficiency.\n\n3. Comprehensive evaluation across diverse datasets and tasks, including both seen and unseen settings. Outperforms 13 strong baselines with significant margins.\n\n4. Ablation studies isolate the impact of each module. Communication/runtime benchmarks support claims of scalability and efficiency.\n\n5. The paper acknowledges the privacy/utility tradeoff introduced by TPA and justifies it well.\n\nQUESTIONS:\n1. It would strengthen the paper to include qualitative examples of LLM/SLM outputs on different TDP tasks.\n\n2. Clarify whether training on seen tasks uses multitask objectives, and whether different task formats are handled with unified prompt templates or adapters.\n\n3. Discuss robustness to noisy or adversarial input, especially since privacy and integrity are core themes.\n\n4. Specify training data volume and compute cost for the LLM and SLM to better assess scalability.\n\nLIMITATIONS:\n1. While TPA and secret-sharing are presented as privacy-preserving, no formal privacy guarantee (e.g., differential privacy bounds) is provided in the main paper.\n\n2. The actual mechanism of how trajectory knowledge is transferred into the LLM (e.g., dataset format, prompt templates, finetuning details) is high-level. No clear justification for the selected base models (LLaMA, GPT3-Small) beyond empirical performance.\n\n3. Although the framework is positioned as “unified,” it does not explore general trajectory analytics like clustering, forecasting, or anomaly explanation. TDP is a narrow subset of trajectory learning, which limits impact.\n\n4. No analysis or examples are given to explain what the LLM/SLM actually learns, or how/why it succeeds on unseen datasets. This matters given the opaque nature of LLM-based solutions in safety-sensitive applications (e.g., transport systems).", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a data preparation approach for trajectory data, aiming to protect privacy and the generalizability to different tasks. Specifically, the paper leverages a federated learning framework to train an auto-encoder that converts each trajectory to a low-dimensional representation. Secret sharing is used to protect privacy during model aggregation. The paper further trains large-language models (LLMs) to improve the generalizability of the proposed approach to different tasks. The privacy analysis is done theoretically, and extensive experiments are performed to justify the proposed approach.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n\n1.\tThe paper combines multiple techniques, e.g., federated learning, large language models, etc., to address an important engineering problem, i.e., how to prepare trajectory data while preserving privacy. \n\n2.\tThe proposed method has been extensively tested in a number of existing trajectory datasets and compared to several key benchmarks. The experiment appears solid. \n\nWeakness \n\n1.\tThe problem setting where trajectory data is divided into different regions is not adequately motivated. The paper mentions “due to legal constraints” , but it is not clear what constraints and why. Also, it is not clear how prevalent the problem setting is. From my understanding, trajectory datasets are more horizontally divided (i.e., each data owner or company has trajectories from a set of individuals) rather than vertically divided (i.e., each dataset contains spatially segmented trajectories). \n\n2.\tThe designed F-TDP approach seems to be a universal approach that applies not only to trajectory data. In other words, it is not clear what specific properties of trajectory data have enhanced the proposed approach on the privacy part. If there is, please clearly specify. \n\n3.\tThe methodology of the paper combines multiple techniques. Although the methodology is overall valid, there does not seem to be a strong contribution in each of the technologies or in the synthetic integration of the techniques. Hence, the methodological contribution appears to be relatively weak.\n\nQUESTIONS:\n1.\tLine 125, the notation D = {D_1, D_2, …} -> {T_1, T_2, …} is not explained. Moreover, it would be more reader-friendly to define what each element exactly means, and how many elements are contained in each set. For example, does T1 represent the first trajectory, and D1 represent the dataset of the first client?\n\n2.\tLines 176-179, the authors mentioned that the advantage of the proposed algorithm over differential privacy (DP) is that DP introduces noise. However, the proposed autoencoder-based strategy essentially also added noise, as the exact trajectory may not be reconstructed from the output of the autoencoder. This undermines the contribution of the proposed method. \n\n3.\tThe paper seems to have an underlying assumption that the property of a full trajectory (i.e., with its origin and destination in a region) is the same as a partial trajectory (i.e., only part of a full trajectory), so the property of both can be extracted from a single auto-encoder in Section 4.1. However, this may not be true, as travelers make route choice decisions more based on their origin and destination, and the route choice on the full trajectory may not be decomposed into each sub-trajectory.  \n\n4.\tIt is not clear in Section 4.1 whether and how the sub-trajectories of an entire trajectory are aligned across multiple clients. \n\n5.\tMinor: Table 1 is a bit hard to read. It is not clear which row in one column corresponds to which row in another column. Please consider reorganizing the table.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n- The paper proposes a privacy preserving framework for trajectory data preparation (Trajectory Privacy AutoEncoder), utilizing federated learning, multi-party computation to prevent information leakage from embeddings.\n- The paper uses LLM models (Trajectory Knowledge Enhancer) to understand trajectory data, and apply on issues such as noise filtering and stay point detection. \n- As a last step, Federated Parallel Optimization improves training efficiency by minimizing data transmission and enabling parallel execution.\n- The authors demonstrate that the given setup out-performs state-of-the-art baselines, improving performance ranging from 4.84% to 45.22% across 10 mainstream TDP tasks.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n- The paper unifies trajectory data planning with privacy centric designs and LLM inferences. The framework is also versatile to multiple TDP related tasks. \n- Performance improvement over baselines are evident and clearly presented. The clarity of paper writing is excellent and ideas are well-presented.\n\n\nWeakness\n- The paper lacks discussion on the computation overhead introduced by the additional measurements (e.g. LLM, privacy preserving protocols). \n- It’s unclear whether the performance improvement is a result of superior framework, or LLM’s inherent capability. The non-LLM baselines can’t leverage strong contextual capabilities of the LLMs, while the LLM baselines are not TDP specific, and it’s unclear how they are applied to TDP tasks.\n\nQUESTIONS:\n- How does introducing LLM affect training and inference speed compared to baselines? \n- How does federated learning + multi-party computation affect the computation overhead? How many rounds of communication are required and what is the communication data size? \n- How are the LLM baselines applied to TDP related tasks, since many of them are generic spatio-temporal understanding tasks?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}