Title: Privacy-Preserving and Effective Cross-City Traffic Knowledge Transfer via Federated Learning
Venue: NeurIPS 2025
Accepted: no
Keywords: Traffic Prediction

Transfer Learning

Federated Learning
Abstract: Traffic prediction aims to forecast future traffic conditions using historical traffic data, serving a crucial role in urban computing and transportation management. While transfer learning and federated learning have been employed to address the scarcity of traffic data by transferring traffic knowledge from data-rich to data-scarce cities without traffic data exchange, existing approaches in Federated Traffic Knowledge Transfer (FTT) still face several critical challenges such as potential privacy leakage, cross-city data distribution discrepancies, and low data quality, hindering their practical application in real-world scenarios. To this end, we present FedTT, a novel privacy-aware and efficient federated learning framework for cross-city traffic knowledge transfer. Specifically, our proposed framework includes three key innovations: (i) a traffic view imputation method for missing traffic data completion to enhance data quality, (ii) a traffic domain adapter for uniform traffic data transformation to address data distribution discrepancies, and (iii) a traffic secret aggregation protocol for secure traffic data aggregation to safeguard data privacy. Extensive experiments on 4 real-world datasets demonstrate that the proposed FedTT framework outperforms the 14 state-of-the-art baselines. All code and data are available at https://anonymous.4open.science/r/FedTT.

1

Abstract

Traffic prediction aims to forecast future traffic conditions using historical traffic data, serving a crucial role in urban computing and transportation management. While transfer learning and federated learning have been employed to address the scarcity of traffic data by transferring traffic knowledge from data-rich to datascarce cities without traffic data exchange, existing approaches in Federated Traffic Knowledge Transfer (FTT) still face several critical challenges such as potential privacy leakage, cross-city data distribution discrepancies, and low data quality, hindering their practical application in real-world scenarios. To this end, we present FedTT, a novel privacy-aware and efficient federated learning framework for crosscity traffic knowledge transfer. Specifically, our proposed framework includes three key innovations: (i) a traffic view imputation method for missing traffic data completion to enhance data quality, (ii) a traffic domain adapter for uniform traffic data transformation to address data distribution discrepancies, and (iii) a traffic secret aggregation protocol for secure traffic data aggregation to safeguard data privacy. Extensive experiments on 4 real-world datasets demonstrate that the proposed FedTT framework outperforms the 14 state-of-the-art baselines. All code and data are available at https://anonymous.4open.science/r/FedTT .

1 Introduction

Traffic Prediction (TP) [70, 51, 80] leverages widespread sensors in the road network to forecast traffic conditions based on historical traffic data (e.g. traffic flow, speed, and occupancy), which not only facilitates the effective allocation of public transportation resources [45] but also contributes to alleviating traffic congestion [74]. To achieve accurate TP, numerous methods have been proposed [80, 23, 24], which typically rely on a large number of traffic data to train high-performing traffic models. However, urban traffic data is often insufficient or unavailable [36, 63, 65], particularly in emerging cities, such as developing regions in the Midwestern United States [1], where sensors are newly deployed or data collection is still in its early stages. In such cases, training traffic models becomes particularly challenging and prone to overfitting, limiting the accuracy of TP tasks [27, 46].

Transfer Learning (TL) [59, 13], a 28 knowledge transfer paradigm, has been 29 widely adopted in TP scenarios to ad30 dress the scarcity of traffic data. To im31 prove the performance of traffic models 32 in data-scarce target cities, existing TL33 based TP methods [41, 43, 57] transfer 34 traffic knowledge from data-rich source 35 cities to target cities, which typically rely 36 on centralized frameworks and involve 37 the exchange of traffic data among cities 38

FT-AED

(Nashville)

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However, the direct sharing of traffic data risks privacy leak- age [39, 45, 70] as such data may contain sensitive personal information.

For example, sparse traffic flow data may allow attackers to infer the presence and approximate locations of individual

vehicles [6, 7]. Besides, many privacy laws and regulations, such as GDPR [5] and CCPA [4], man- date data collectors to minimize non-essential data transmission and avoid centralized data storage.

Therefore, maintaining the decentralization of traffic data in TP is critical. As shown in Fig. 1(a),

PeMSD4 [3], FT-AED [12], HK-Traffic [2], and PeMSD8 [3] are four real-world traffic datasets, which correspond to the cities of San Francisco (

Bernardino (

SB

SF

), Nashville (

NV

), Hong Kong (

HK

), and San

), respectively. Among these, SF, NV, and HK represent source cities, while SB serves as the target city. Due to legal restrictions, traffic data cannot be exchanged among cities, meaning

each city can only access its local data. In this case, transferring traffic knowledge from these three source cities to the target city without exchanging raw traffic data becomes challenging.

Federated Learning (FL) [68, 37, 70], a privacy-preserving distributed learning paradigm, has been 51 widely used in numerous applications to address privacy concerns such as urban computing [66] and 52 transportation management [70]. For instance, JD Company (one of the largest e-commerce compa53 nies in China) developed the Fedlearn platform to help protect data privacy for TP applications [20]. 54 Inspired by its success, recent studies [49, 78] have explored the FL framework to transfer traffic 55 knowledge while preserving data privacy, which typically follow a two-stage process, as illustrated in 56 Fig. 1(b). In the first stage, the three source cities (i.e., SF, NV , and HK), as clients, use their local 57 traffic data to train individual local models. Subsequently, clients upload training gradients or model 58 parameters to a central server, which aggregates to a global traffic model and then broadcasts the 59 global model back to clients for local model updates. This process iterates until the global model 60 converges. In the second stage, the converged global model is shared with the target city (i.e., SB) 61 and further fine-tuned using its local traffic data. While this two-stage knowledge transfer framework 62 has become the mainstream approach in Federated Traffic Knowledge Transfer (FTT), it faces 63 three unresolved challenges, i.e., privacy, effectiveness, and robustness, that hinder its application in 64 real-world traffic knowledge transfer scenarios, as illustrated in Fig. 2. 65

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Challenge 1: How to effectively protect data privacy in FTT? Although existing methods utilize FL 66 to avoid raw data exchange, there remains a potential risk of data privacy leakage. This arises because 67 these methods require the uploading of training gradients or model parameters for aggregation in FTT, 68 which may allow attackers to infer raw data by inference attacks [18, 67, 81], as depicted in Fig. 2(a). 69 To mitigate this risk, a straightforward approach is to apply privacy-preserving techniques such as 70 Homomorphic Encryption (HE) [52] and Differential Privacy (DP) [16] for secure aggregation on 71 the uploaded data. However, HE introduces significant computation and communication overheads, 72 which diminishes training efficiency, while DP lowers data utility and thus decreases model accuracy, 73 as proved by previous studies [64, 58, 15]. Therefore, how to effectively safeguard data privacy in 74 75

FTT without compromising training efficiency and model accuracy remains a significant challenge. Challenge 2: How to mitigate the impact of cross-city data distribution discrepancies on FTT? 76 None of the previous studies have considered the discrepancies in traffic data distribution across 77 cities, which decreases the effectiveness of traffic knowledge transfer [41, 43, 57]. Specifically, the 78 traffic domain varies significantly across cities, with distinct distributions of traffic flow, speed, and 79 occupancy data. As shown in Fig. 2(b), we illustrate the frequency density distribution of traffic speed 80 data for SF, NV, HK, and SB. As observed, SF and SB exhibit similar data distributions, suggesting 81 closely related traffic domains, while NV and SB show different data distributions, indicating quite 82 distinct traffic domains. Consequently, traffic knowledge transfer from SF to SB results in smaller 83 prediction errors and is more effective than the transfer from NV to SB. Overall, how to address 84 traffic domain discrepancies across cities to improve the effectiveness of FTT is an urgent challenge. 85

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Challenge 3: How to overcome low traffic data quality issues in FTT? Existing methods assume that traffic data is consistently high-quality and reliable, neglecting the prevalence of missing data. As shown in Fig. 2(c), we illustrate the number of available sensors over a week in HK, which has 411 sensors in total. Due to sensor failures or updates [73, 50], the number of available sensors in HK may fluctuate over time, disrupting the model training process. While some data imputation methods [8, 48, 73] can be employed to complete missing data, they fail to effectively capture the spatio-temporal dependencies inherent in traffic data, leading to suboptimal accuracy. Consequently, how to enhance the traffic data quality to improve the robustness of FTT is another challenge.

Contributions. To address these challenges, we propose FedTT, a privacy-preserving and efficient Federated learning framework for cross-city Traffic knowledge Transfer. Unlike existing FTT methods, FedTT transforms the traffic data from the source cities' domain to the target city's domain and training the target city's model on the transformed data. To address Challenge 1 , FedTT introduces the Traffic Secret Aggregation (TSA) protocol to securely aggregate the transformed data without compromising training efficiency or model accuracy. To overcome Challenge 2 , FedTT develops the Traffic Domain Adapter (TDA) to uniformly transform the traffic data from source cities' domains to that of the target city through traffic domain transformation, alignment, and classification. To deal with Challenge 3 , FedTT designs the Traffic View Imputation (TVI) method to complete missing traffic data by capturing the spatio-temporal dependencies. Finally, extensive experiments conducted on 4 real-world datasets demonstrate that FedTT achieves state-of-the-art performance, reducing prediction MAE by 5.43% to 75.24% and maintaining Pearson Correlation Coefficient (PCC) of data reconstruction attacks at no more than 10% compared to 14 baseline methods.

2 Problem Definitions

The frequently used notations and descriptions in this paper are shown in Appendix B .

Definition 1 (Road Network). The road network is a weighted graph G = ( M , E , A ) , where M = { m 1 , m 2 , . . . } is the set of sensors, E ⊆ M × M is the set of edges, and A ∈ R |M|×|M| is the weighted adjacency matrix of edges. Here, m i denotes the sensor with index i .

Definition 2 (Traffic Data). Given the available sensors M t = { m i | i ≤ |M|} , the traffic data is denoted as X = { X 1 , X 2 , . . . } , where X t ∈ R | M t |× F 1 is the traffic data of | M t | available sensors at time t . Here, F 1 denotes the number of traffic data features. For instance, F 1 = 3 when the traffic data includes flow, speed, and occupancy data.

Problem Formulation (FTT). In federated learning, multiple clients C = { c 1 , c 2 , . . . , c n } collaboratively train a global model using their local data. In the first stage, FTT trains a traffic model θ TP to learn traffic knowledge from source cities R = { R 1 , R 2 , . . . , R n } , where each source city R i corresponds to a client c i , as formally shown below:

where L ( · ) is the loss function, and D R i = { X R i 1 , X R i 2 , . . . ; G R i } is the traffic dataset of the source city R i . Here, G R i and X R i t are the road network and the traffic data at time t of the source city R i . In the second stage, given target city' dataset D S = { X S 1 , X S 2 , . . . ; G S } , FTT predicts the next T ′ traffic data based on the T historical observations at time t in the target city S , as shown below:

3 Our Methods

Fig. 3 illustrates the architecture of the proposed FedTT framework, which comprises three modules: Traffic View Imputation (TVI), Traffic Domain Adapter (TDA), and Traffic Secret Aggregation (TSA). As shown in Fig. 3(a), FedTT comprises n clients C = { c 1 , c 2 , . . . , c n } and a central server s . Specifically, each source city R i is treated as a client c i , while the target city S is treated as the server s . The traffic domains of the data in clients are transformed to align with the server's domain, and the server's traffic model is trained on this transformed data uploaded by clients. Consequently, the FTT problem defined in Eqs. 1 and 2 is reformulated to minimize the sum of the following losses:

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)

where D R i → S represents the traffic dataset whose domain is transformed from the source city R i to the target city S . The overall process of FedTDP is as following. First, the TVI module captures spatial and temporal dependencies within the traffic data to extend and enhance the traffic view ( ① -② ), as shown in Fig. 3(b). Then, the TDA module conducts traffic domain transformation and alignment for the source cities' data ( ③ -④ ). Besides, the module performs traffic domain classification to categorize the traffic data domain ( ⑤ ), as shown in Fig. 3(c). Finally, the TSA module employs the proposed traffic secret aggregation method to securely mask and aggregate the transformed data from source cities ( ⑥ -⑦ ), as shown in Fig. 3(d). The target of our FedTT is to transfer traffic knowledge across cities while preserving privacy, handling data discrepancies and low data quality challenges.

3.1 Traffic View Imputation

Design Motivation. Existing federated traffic transfer methods often overlook the challenges associated with low-quality traffic data, especially when missing data is prevalent, thereby significantly undermining the performance of traffic knowledge transfer models. Although some data augmentation methods [8, 48, 73] can be leveraged for imputation, they fail to effectively capture the spatio-temporal dependencies of data, leading to suboptimal accuracy. In contrast, we propose the Traffic View Imputation (TVI) method to enhance traffic data quality by

(a) Spatial view extension

(b) Temporal view enhancement

Extend

|

M

GAT

Dijkstra

t

t+T

t+T-1

t+1

t

t-T

t-T+1

t-1

Road Network

Shortest Distance Matrix

Traffic Data

Unavailable Data

t

t+1

t-1

Traffic View

Enhance

Figure 4: The process of traffic view imputation completing missing traffic data through a comprehensive exploration of the spatial and temporal dependencies inherent in traffic data:

where θ TVI is the TVI model consisting of a spatial view extension model θ SV and a temporal view enhancement model θ TV . Besides, ˜ X t is the imputed traffic data of all sensors. In addition, the traffic view represents the traffic data of all sensors at a certain time, as defined below.

Definition 4 (Traffic View). A traffic view is the snapshot of traffic data of sensors M at time t , consisting of a set of multi-level traffic subviews, denoted as V t = { v 1 t , v 2 t , . . . v | M t | t } , where i -level traffic subview v i t is a set of traffic data of i sensors at time t .

where θ SV denotes the spatial view extension model and sv |M| t represents the extended |M| -level traffic subview at time t . As shown in Fig. 4(a), it first computes the shortest distance matrix A = { A 1 , A 2 , . . . , A |M| } , where A i represents the shortest distance tensor of sensor m i to other sensors. This is computed using Dijkstra's algorithm [14] with the weighted adjacency matrix A . Next, the feature of each sensor is computed, i.e., h i = θ GAT ( A i ) , where h i represents the K -head feature of sensor m i with F 2 feature dimensions, and θ GAT is the Graph Attention Network (GAT) model [61] with K = 8 and F 2 = 128 . Additionally, the extension of multi-level traffic subviews is averaged to obtain the |M| -level traffic subview with a Multi-Layer Perception (MLP [54]) θ E :

where v i t [ j ][ k ] represents the traffic data of the k -th sensor in the j -th combination within the i -level 171 traffic subview at time t , and H ( v i t [ j ][ k ]) ∈ R K × F 2 × 1 represents the multi-head feature of the sensor 172 corresponding to v i t [ j ][ k ] . Finally, it computes the loss of available sensors to train the θ SV model: 173

where V SV = { sv |M| 1 , sv |M| 2 , . . . } is the set of extended traffic subviews at different times, and sv | M t | t is the predicted traffic data of available sensors at time t .

ii) Temporal View Enhancement. As shown in Fig. 4(b), in the second stage, TVI enhances the |M| -level traffic subview based on the preceding/succeeding T |M| -level traffic subviews:

where tv |M| t represents the enhanced |M| -level traffic subview, whose final value is the average of the above two results. Besides, θ TV is the temporal view enhancement model, which employs the SOTA DyHSL traffic model [80]. Then, it computes the loss of available sensors to train the θ TV model:

where V TV = { tv |M| 1 , tv |M| 2 , . . . } represents the set of enhanced traffic subviews and tv | M t | t is the predicted traffic data of the available sensors at time t . Finally, we get the predicted traffic data of all |M| sensors ˜ X t = tv |M| t . Note that the training of the TVI model is completed before the training of the FedTT framework, as it only needs to be conducted within each city.

3.2 Traffic Domain Adapter

Design Motivation. None of the existing approaches consider traffic data distribution discrepancies between the source and target cities in FTT, which decreases the effectiveness of traffic knowledge transfer. Motivated by this, to reduce the impact of traffic data distribution discrepancies on model performance, we propose the Traffic Domain Adapter (TDA) module, as shown in Fig. 5. This module reduces traffic domain discrepancies by uniformly transforming data from the traffic domain of the source city ("source domain" for short) to the traffic domain of the target city ("target domain" for short):

Server

s

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Client

c

i

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Align

Classify

Traffic Domain Prototype

Aggregated Data

Target Data

Data Communication

G

Mix

D

Discriminator

Generator

D

G

Individual?

Aggregated?

Source?

Target?

Mask

Source Data

②

⑧

①

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③

⑦

④

Mix

⑤

Classify

D

⑥

· · ·

Mixed Traffic Data

Mixed Traffic Data

where X R → S t is the transformed data of |M S | sensors, and θ TDA is a generative adversarial network [62] consisting of a generator model θ Gen and a discriminator model θ Dis .

i) Traffic Domain Transformation. In the first step, TDA uses the generator model, road network, and traffic domain prototype to transform the traffic data from the source domain to the target domain, as shown in Fig. 5 ( ① ), where the traffic domain prototype is the representative traffic sample that can reflect the main feature of traffic data in the domain, as formally defined below.

Definition 5 (Traffic Domain Prototype). Given the traffic data X = { X 1 , X 2 , . . . } in a traffic domain, a traffic domain prototype P is the central traffic data, which is computed as the averaged value of all traffic data, i.e., P = 1 |X| ∑ |X| t =1 X t .

First, it computes the transformation matrix A G of the road network through ( A G ) ⊤ · G R · A G = G S , where A G can learn the road network information of the source and target cities, which is computed by the gradient descent method [53]. Similarly, it then computes the transformation matrix A P of the traffic domain prototype through A P · P R = P S , where P R and P S are traffic domain prototypes of

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the source and target cities, respectively. Here, A P can learn the traffic domain prototype information of the source and target cities, which is computed by the gradient descent method. Then, the generator model leverages A G and A P to transform the traffic data using MLP models θ G , θ P , and θ X :

ii) Traffic Domain Alignment. In the second step, TDA trains the generator model θ Gen , as shown in Fig. 5 ( ② ). Specifically, it aligns the transformed data X R → S = { X R → S 1 , X R → S 2 , . . . } of the source city with the traffic domain prototype P S of the target city S , as described below:

iii) Traffic Domain Classification. In the third step, TDA trains the discriminator model θ Dis to classify the traffic data domain ( ⑤ -⑥ shown in Fig. 5), as shown below:

where X RS = { X RS 1 , X RS 2 , . . . } is the traffic data mixed with the transformed data X R → S of the source city and the traffic data X S of the target city. Besides, discriminator model θ Dis is a MLP model. Then, the training process of θ Dis is shown below:

Next, we update the training process of the generator model θ Gen in Eq. 12, as shown below:

where λ 1 is the hyperparameter to control the trade-off between generator loss and discriminator loss.

3.3 Traffic Secret Aggregation

Design Motivation. Existing works upload gradients or models for aggregation in FTT, where attackers derive the traffic data through inference attacks [18, 67, 81]. Although techniques such as Homomorphic Encryption (HE) [52] and Differential Privacy (DP) [16] can be employed for secure aggregation, they come with notable trade-offs. Specifically, HE introduces significant computational and communication overheads, reducing training efficiency, while DP reduces the data utility, leading to lower model accuracy. In contrast, we design the Traffic Secret Aggregation (TSA) protocol that securely transmits and aggregates the transformed data from source cities to protect traffic data privacy without sacrificing the training efficiency or model accuracy, as shown in Fig. 5 ( ③ -④ ).

Specifically, it first masks the r -th transformed data R i X R i → S ( r ) in the client c i , as shown below:

where X R→ S ( r ) is r -th aggregated data. Besides, X ( R→ S, R i ) ( r ) is the r -th mask data computed in the client c i and transmitted to the server. Note that, when r = 0 , the client uses HE to encrypt its transformed data and transmitted the encrypted data to the server for initial aggregation. Then, the server computes the sum of mask data from all source cities, as shown below:

Finally, the server gets the r -th aggregated data using the previous aggregated data, as shown below: 239

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In this way, it ensures that only the aggregated data can be accessed without revealing the individual transformed data. Besides, the client c i can train a local discriminator model θ R i Dis to classify the aggregated data and individual transformed data ( ⑦ -⑧ shown in Fig. 5), as shown below:

where X R i S = { X R i S 1 , X R i S 2 , . . . } is the traffic data mixed with the aggregated data X R→ S and transformed data X R i → S . Besides, θ R i Dis is a MLP model and its training process is shown below:

Therefore, given the traffic data X R S = { X R S 1 , X R S 2 , . . . } consisting of aggregated data X R→ S and traffic data X S , the updated training process of the generator model θ Gen in Eq. 15 is shown below:

where θ R i Gen and θ Dis are the local generator model and global discriminator model in the client c i and server s , respectively. Here, λ 1 and λ 2 are the hyperparameter to control the trade-off between generator loss and discriminator loss.

The overall training process and theoretical privacy analysis of FedTT are shown in Appendix C .

4 Experiment

Datasets. We use four traffic datasets to evaluate the proposed FedTT framework in experiments, which are widely used in traffic prediction tasks [80, 23, 24], as shown in Table 1. Specifically, PeMSD4 ( P4 ) [3], PeMSD8 ( P8 ) [3], FT-AED ( FT ) [12], and HK-Traffic ( HK ) [2] were collected in the San Francisco, San Bernardino, Nashville, and Hong Kong, respectively. Among them, three datasets are considered as three source cities, and one dataset serves as the target city, leading to four scenarios: (P8, FT, HK) → P4, (P4, FT, HK) → P8, (P4, P8, HK) → FT, and (P4, P8, FT) → HK. Besides, we select traffic flow, speed, and occupancy prediction tasks for experiments, which are also widely studied in the community [80, 23, 24]. In addition, we report the rate of missing traffic data in these datasets, which reveals varying levels of traffic data quality issues.

Baselines. We compare FedTT with (i) three SOTA methods in FTT including T-ISTGNN [49], pFedCTP [78], and 2MGTCN [75], (ii) three SOTA Multi-Source Traffic Knowledge Transfer methods (MTT) extended for the FTT problem including TPB [41], ST-GFSL [43], and DastNet [57], and (iii) three SOTA Single-Source Traffic Knowledge Transfer methods (STT) for the FTT problem including CityTrans [47], TransGTR [27], and MGAT [46]. In addition, we replace the TVI module of FedTT with three SOTA data imputation methods (LATC [8], GCASTN [48], and Nuhuo [73]) to evaluate its effects. More details about these baselines are provided in Appendix D.1 .

Evaluation Metrics. We use Mean Absolute Error (MAE), Root Mean Square Error (RMSE), communication size (GB), and running time (minutes) to evaluate the utility in experiments. Besides, Mean Square Error (MSE) and Pearson Correlation Coefficient (PCC) between the reconstructed data and the ground truth data to measure the privacy-preserving ability of different methods.

Implementation. All baselines run under their optimal settings. Besides, we use 5% train data, 10% validation data, and 10% test data in the target city. In addition, the MLP model used in FedTT is three-layer with the GELU [21] activation and 1024 hidden dimensions. Moreover, all experiments are conducted with four nodes, one as a server and the other three nodes as clients, each equipped with two Intel Xeon CPU E5-2650 12-core processors and two NVIDIA GeForce RTX 3090.

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4.1 Overall Performance

To show the overall performance of different methods on traffic flow, speed, and occupancy ("occ" for short) predictions tasks, we take 60 minutes (12-time steps) of historical data as input and output the traffic prediction in the next 15 minutes (3-time steps), as shown in Table 2, where the best results are shown in blue. Here, the DyHSL [80] model is implemented in FedTT as it achieves the state-ofthe-art performance in the centralized traffic model. As observed, the proposed FedTT framework achieves the best performance on different traffic datasets and traffic prediction tasks compared to other methods, showing its effectiveness of traffic knowledge transfer in the FTT problem, i.e., the gains range from 5.43% to 75.24% in MAE and 2.63% to 67.54% in RMSE.

4.2 Privacy Protection Study

To evaluate the privacy-preserving capabilities, we conduct the data reconstruction attack to different methods across datasets on traffic flow prediction using MSE and PCC, as illustrated in Fig. 6. As observed, FedTT demonstrates robust resistance to the data reconstruction attack, achieving a high MSE and maintaining a PCC within 2.17% to 8.81% , not exceeding 10%, while other methods exhibit weaker defenses, with a lower MSE and PCC larger than 40%.

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These findings underscore the superiority and effectiveness of privacy protection provided by the proposed FedTT framework in FTT and highlight the limitations of privacy preservation mechanisms based solely on traditional federated learning frameworks.

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(a)Effect of modules

(b) Communication size

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(d) Data imputation study

4.3 Ablation Study 300

Fig. 7 shows the ablation study, where we removed the module of FedTT one at a time, namely FedTT 301 without TVI (w/o TVI), FedTT without TDA (w/o TDA), and FedTT without TSA (w/o TSA). First, 302 when TVI is absent, MAE increases by 1.49% to 9.23% , underscoring its pivotal role as an effective 303 way to complete the missing data. Besides, the training of TVI is completed before the FedTT's 304 training as it only needs to be conducted within each source city, thus not increasing communication 305

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overhead or running time during FedTT's training. Additionally, compared to other data imputation methods (i.e., LATC, GCASTN, and Nuhuo), FedTT with TVI achieves better performance, showing its effectiveness in the traffic data completion. Second, when TDA is removed, MAE increases by 4.46% to 17.86% , which demonstrates its effectiveness in addressing traffic data distribution differences. Besides, communication overhead and running time of FedTT slightly increase compared to w/o TDA. Third, MAE of FedTT decreases 0.66% to 3.76% compared to w/o TSA as TSA uses the averaged source data, which reduces the influence of source city's traffic patterns on the target city's model training. Besides, the communication overhead and running time of FedTT compared to w/o TSA do not change as TSA is a lightweight module for federated secure aggregation.

4.4 Long-Term Traffic Prediction

To evaluate long-term traffic prediction capabilities, we illustrate the performance of different methods over the next 60 minutes (12 time steps) for traffic flow and speed prediction using MAE, as shown in Fig. 8. As observed, FedTT outperforms all other methods, i.e., the gains range from 5.03% to 64.41% , showing its effectiveness of long-term traffic prediction in FTT. Therefore, the proposed FedTT framework demonstrates strong performance in both

FedTT

pFedCTP

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td←

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→→HK

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→HK

(a)Flowprediction

(b)Speedprediction

4.5 Model Scalability

To validate the model scalability, we show the traffic flow and speed prediction performance of different methods across different sizes of training data in the target city, ranging from 5% to 40% in the (P8, FT, HK) → P4 scenario using MAE, as shown in Fig. 9. As observed, the FedTT framework consistently achieves the best performance in different-scale datasets with 7.22% to 49.26% MAE less than other methods, indicating its superior scalability in FTT.

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(a)Flowprediction

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Besides, as the size of the training data increases, all methods exhibit improved performance. This is because more training data enhances the model learning capability on the target city's traffic pattern.

4.6 More Experiments

We conduct more experiments to comprehensive evaluate FedTT, in terms of model adaptability, efficiency, hyperparameter sensitivity, and case study: i) Appendix D.2 demonstrates the performance when extending different centralized traffic models to FedTT and the two-stage transfer of existing methods in FTT, where FedTT achieves 5.13% to 64.65% lower MAE in all models. ii) Appendix D.3 shows the efficiency of different methods, where FedTT reduces communication overhead by 90% and running time by 1 to 2 orders of magnitude compared to all baselines. iii) Appendix D.4 shows the FedTT's performance with different hyperparameter settings, where λ 1 = 0 . 7 and λ 2 = 0 . 4 are optimum values. iv) Appendix D.5 showcases FedTT's practical efficacy in a real-world scenario.

5 Conclusion and Limitations

In this paper, we propose FedTT, a privacy-aware and efficient federated learning framework for crosscity traffic knowledge transfer. It includes a traffic view imputation method to enhance data quality, a traffic domain adapter to address data distribution discrepancies, and a traffic secret aggregation protocol to safeguard data privacy. Experiments using 4 datasets demonstrate its superiority. Our work has several limitations that warrant further exploration. First, we have not addressed gridbased scenarios, which could be an important direction for future research. Besides, while our study primarily focuses on traffic prediction tasks, extending the framework to support more spatio-temporal prediction tasks remains an open opportunity. In addition, we have not systematically evaluated the impact of varying the number of source cities on the performance of traffic knowledge transfer, which could provide additional insights into the scalability of the proposed framework.

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Appendix

In the subsequent sections, we present supplementary materials to provide more details of this paper, offering deeper insights and additional technical details for readers seeking further clarification. The appendix is organized as follows.

In Section A , we present a systematic review of related work to help readers understand the key development in areas relevant to this paper, including traffic prediction and traffic knowledge transfer.

In Section B , we summary the frequently used notations and descriptions for better understanding our work.

In Section C , we provide the additional methodology details of our proposed FedTT framework, including (i) the federated parallel training strategy, (ii) the training process with training algorithm and complexity analysis, and (iii) the theoretical privacy analysis.

In Section D , we describe the extensive experimental details to provide more information about experimental settings and further demonstrate the superiority performance of the proposed FedTT framework, including (i) compared baselines introduction, (ii) the details experimental results of model adaptability, efficiency, hyperparameter sensitivity, and case studies.

A Related Work

A.1 Traffic Prediction

Traffic prediction plays a critical role in the development of smart cities and has garnered significant attention in the spatio-temporal data mining community. Currently, deep learning techniques [54] are widely employed in traffic prediction tasks. Convolutional models, such as Convolutional Neural Networks (CNN) [33] and Graph Convolutional Networks (GCN) [29], are used to capture spatial correlations in traffic time-series data. Meanwhile, sequential models including Gated Recurrent Units (GRU) [11] and Long Short-Term Memory (LSTM) [19], are employed to extract temporal dependencies from the data. Several advanced models have achieved state-of-the-art performance. For instance, ST-SSL [23] improves traffic pattern representation to account for spatial and temporal heterogeneity through a self-supervised learning framework. DyHSL [80] leverages hypergraph structure information to model the dynamics of a traffic network, updating the representation of each node by aggregating messages from associated hyperedges. Additionally, PDFormer [24] introduces a spatial self-attention module to capture dynamic spatial dependencies and a flow-delay-aware feature transformation module to model the time delays in spatial information propagation. Since this paper is not intended to propose another more complex prediction model, a detailed analysis of existing traffic prediction models can be found in surveys [25, 28]. However, these models are centralized and rely on traffic data uploads from sensors to a central server, which poses a risk of data leakage.

To address data privacy concerns, several traffic prediction studies [74, 31, 69, 34, 70, 38] in federated environments have been proposed. Specifically, FedGRU [39] pioneers the integration of GRU into FL for TP tasks, employing federated averaging to aggregate models and a joint announcement protocol to enhance model scalability. Subsequently, CNFGNN [45] separates the modeling of temporal dynamics on the device from spatial dynamics on the server, using alternating optimizations to reduce communication costs and facilitate computation on edge devices. Moreover, FedGTP [70] promotes the adaptive exploitation of inter-client spatial dependencies to enhance prediction performance while ensuring data privacy. However, urban traffic data is often insufficient or unavailable, particularly in emerging cities. Training traffic models in these data-scarce cities is prone to overfitting, which undermines model performance and affects the accuracy of TP tasks. In contrast, we aim to propose a federated traffic prediction framework that efficiently transfers traffic knowledge from data-rich cities to data-scarce cities, enhancing TP capabilities for the latter.

A.2 Traffic Knowledge Transfer

Transfer learning can enhance the traffic model capabilities of data-scarce target cities by transferring traffic knowledge from data-rich source cities in traffic prediction tasks. Existing studies can be broadly categorized into three types: Single-Source Traffic Knowledge Transfer (STT), Multi-

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Source Traffic Knowledge Transfer (MTT), and Federated Traffic Knowledge Transfer (FTT), in chronological order from earliest to most recent.

First, STT [26, 17, 63, 36, 72, 22, 35, 9, 65] studies focus on transferring traffic knowledge from a single source city to a target city. Specifically, TransGTR [27] jointly learns transferable structure generators and forecasting models across cities to enhance prediction performance in data-scarce target cities. Next, CityTrans [47] leverages adaptive spatio-temporal knowledge and domain-invariant features for accurate traffic prediction in data-scarce cities. Additionally, MGAT [46] uses a metalearning algorithm to extract multi-granular regional features from each source city to improve the effectiveness of traffic knowledge transfer. However, the performance of these STT methods can be significantly compromised when there are substantial differences in traffic data distribution between the source and target cities.

Second, MTT [71, 40, 78, 77] studies the joint transfer of traffic knowledge from multiple source cities to a target city, enabling the target city to acquire diverse traffic knowledge and enhancing the robustness of the trained traffic models. Specifically, TPB [41] uses a traffic patch encoder to create a traffic pattern bank, which data-scarce cities query to establish relationships, aggregate meta-knowledge, and construct adjacency matrices for future traffic prediction. Next, ST-GFSL [43] transfers knowledge through parameter matching to retrieve similar spatio-temporal features and defines graph reconstruction loss to guide structure-aware learning. Additionally, DastNet [57] employs graph representation learning and domain adaptation techniques to create domain-invariant embeddings for traffic data. However, these methods rely on centralized frameworks, which involves sharing and exchanging traffic data across cities without considering traffic data privacy.

Third, the latest FTT studies, including T-ISTGNN [49], pFedCTP [78], and 2MGTCN [75], intend to protect data privacy in cross-city traffic knowledge transfer. Specifically, T-ISTGNN [49] combines privacy-preserving traffic knowledge transfer with inductive spatio-temporal GNNs for cross-region traffic prediction. Besides, pFedCTP [78] employs personalized FL to decouple the ST-Net into shared and private components, addressing the spatial and temporal heterogeneity. In addition, 2MGTCN [75] combines multi-modal GCNs and TCNs to capture spatial and temporal information and enhance adaptability across cities. However, they face challenges such as privacy leakage, data distribution discrepancies, low data quality, and high knowledge transfer overhead, making them unsuitable for real-world applications, as shown in Fig 2. In contrast, we aim to propose a privacy-preserving and efficient federated learning framework for cross-city traffic knowledge transfer to address the challenges of privacy, effectiveness, robustness, and efficiency in FTT.

B Notations and Descriptions

We present the frequently used notations and descriptions in this paper, as listed in Table 3. 627

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C Methodology Details

C.1 Federated Parallel Training

To improve the training efficiency, FedTT introduces the federated parallel training strategy to reduce the data transmission and train the models in parallel.

i) Split Learning. To reduce the communication overhead and improve the training efficiency, it employs split learning [45] to decompose the sequential training process into the client and server training, and freeze the data required by the client and server. Specifically, the client c i stores and freezes the data sent by the server for θ R i Gen and θ R i Dis training in Eqs. 21 and Eqs. 20, respectively:

where Fr ( · ) is the frozen function and uses the historical cached data, which updates every 5 rounds. Besides, the server s stores and freezes the data uploaded by the client to compute the aggregated data for θ Dis and traffic model θ TP training in Eqs. 14 and 3, respectively:

ii) Parallel Optimization. To further improve the training parallelism, it proposes parallel optimization to reduce data dependencies on the client and server. Specifically, the client c i caches and freezes the local data for θ R i Gen and θ R i Dis parallel training in Eqs 22 and 23, as shown below:

where Fr ′ ( · ) is the frozen function and uses the historical cached data, which updates each round.

C.2 Training Process

Before the training of the FedTT framework, clients (i.e., source cities) train the spatial view expansion model θ SV and the temporal view expansion model θ TV in the TVI module θ TVI by minimizing the loss in Eqs. 7 and 9, as shown below:

where V SV and V TV are the set of traffic subviews at different times obtained by spatial view extension and temporal view enhancement, respectively. During the training of the FedTT framework, the client c i trains the local generator model θ R i Gen and the local discriminator model θ R i Dis by minimizing the loss in Eqs. 20 and 21, as shown below:

where X R S is the traffic data consisting of the aggregated data X R→ S and traffic data X S of the target city S , and X R i S is the traffic data consisting of the aggregated data X R→ S and transformed data X R i → S of the source city R i . Besides, the server s trains the global discriminator model θ Dis and traffic model θ TP by minimizing the loss in Eqs. 14 and 3, as shown below:

where D R→ S is the aggregated traffic dataset whose traffic domain is transformed from source cities 658 to the target city S , and D S is the traffic dataset of the target city S . 659

Algorithm 1 The training of the FedTT framework in the client c

i 1: Input: the server s (i.e., the target city S ). 2: ˜ X R i ← Complete ( θ TVI , X R i ) // Complete the missing data. 3: for each training round tr = 1 , 2 , . . . do 4: for each data X R i ( r ) ∈ ˜ X R i , r = 1 , 2 , . . . do 5: X R i → S ( r ) ← Transform ( θ R i Gen , X R i ( r ) ) // Transform the traffic data. 6: Classify ( θ R i Dis , X R i → S ( r ) ) // Classify the transformed data. 7: if tr == 1 and r == 1 then 8: E R i → S ( r ) ← Encrypt ( X R i → S ( r ) ) // Encrypt the transformed data. 9: Send ( s, E R i → S ( r ) ) // Send the transformed data. 10: else 11: if tr == 1 and r == 2 then 12: E R→ S ( r -1) ← Get ( s, r ) // Get the encrypted aggregated data. 13: X R→ S ( r -1) ← Decrypt ( E R→ S ( r -1) ) // Decrypt the aggregated data. 14: else 15: X R→ S ( r -1) ← Get ( s, r ) // Get the aggregated data. 16: end if 17: Classify ( θ R i Dis , X R→ S ( r -1) ) // Classify the aggregated data. 18: X ( R→ S,R i ) ( r ) ← X R→ S ( r -1) + X R i → S ( r ) -X R i → S ( r -1) // Mask the transformed data. 19: Send ( s, X ( R→ S, R i ) ( r ) ) // Send the mask data. 20: end if 21: end for 22: end for

Algorithm 2 The training of the FedTT framework in the server s

Training Algorithm . For convenient method reproduction, we provide detailed training Algorithms 1 660 and 2 of the FedTT framework, including the client and server. 661

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In the client (i.e., Algorithm 1), the target city acts as the server (line 1). Before the training process, the client completes the missing traffic data through the traffic view imputation method (line 2). During each training round and each traffic data (lines 3-4), it first transforms the data from the traffic domain of the source city to that of the target city (line 5) and classifies the transformed data using the local discriminator model(line 6). If the training process is in the first round using the first data instance (line 7), the client encrypts the transformed data using homomorphic encryption and sends it to the server (lines 8-9). Otherwise, if the training process is in the first round using the second data instance (lines 10-11), the client gets the encrypted data and decrypts it to get the previous aggregated data (lines 12-13). For subsequent rounds or data instance, the client directly gets the previous aggregated data from the server without decryption (lines 14-16). In either case, it classifies the previous aggregated data using its local discriminator model (line 17). Then it masks the transformed data using the previous aggregated and transformed data (line 18). Finally, it sends the mask data to the server for data aggregation (lines 19-22).

In the server (i.e., Algorithm 2), the source cities act as the clients (line 1). During each training round and each traffic data (lines 2-3), if the training process is in the first round using the first data instance (line 4), the server gets the encrypted data from clients (line 5). Then, it aggregates them by summing up, and send the aggregated encrypted data to back to the clients for further processing (lines 6-7). For subsequent rounds or data instances (line 8), the server gets the mask data from clients (line 9). Then, it aggregates the masked data using the previous aggregated data (line 10). Next, it classifies the aggregated data using its global discriminator model and sends the aggregated data back to the clients (lines 11-14). Finally, at the end of each training round, it classifies local traffic data and performs traffic prediction using the aggregated and local traffic data (lines 15-17).

Complexity Analysis . We also give the complete complexity analysis for the training of the FedTT framework, i.e., Algorithms 1 and 2. For the client (i.e., Algorithm 1), the training complexity is O (( |M R i | + |M S | ) × ( F 1 × H ) 2 × |X R i | ) at each round. For the server (i.e., Algorithm 2), the training complexity is O (( |M S | × ( F 1 × H ) 2 + MC ( θ TP )) × ( |X S | + ∑ n i =1 |X R i | )) at each round. Here, |M R i | and |M S | are the number of sensors in the source city R i and target city S , respectively. Besides, |X R i | and |X S | are the number of traffic data in the source city R i and target city S , respectively. In addition, F 1 = 3 is the dimensions of traffic data features, and H = 1024 is the hidden dimensions of the three-layer MLP model in θ R i Gen and θ R i Dis . Moreover, MC ( θ TP ) is the model complexity of θ TP (i.e., θ DyHSL ).

C.3 Theoretical Privacy Analysis

The privacy protection mechanism of the proposed FedTT framework comprises two stages. First, it uses the Traffic Domain Adapter (TDA) to transform the data from the traffic domain of source cities to that of the target city, where the parameters of the TDA model are private and not shared with the server and other clients. Second, it performs Traffic Secret Aggregation (TSA) to secure mask and aggregate the transformed data. Consequently, an attacker must first reverse-engineer the transformed data from the aggregated data and then infer the original traffic data from the transformed data. To rigorously analyze the privacy-preserving capability of these two stages, we first define the threat model as follows.

Threat Model . Following previous works [76, 60, 79] in federated learning scenarios, we assume that the server acts as a semi-honest adversary who will honestly execute the required operations (e.g., aggregation) but also remains curious about the private data in clients. In the FTT problem, the server may perform inference attacks to infer the raw instance-level traffic data of clients based on the adversary knowledge, including the client model architecture, privacy-preserving mechanism, and the intermediate data (e.g., model parameters or training gradients) uploaded by clients.

Based on this, we analyze the privacy leakage of FedTT using mutual information [30] as follows.

Privacy Protection in Traffic Domain Adapter. Given the transformed data X R i → S of the source city R i , the attacker aims to infer the original traffic data X R i , where X R i → S is derived from X R i in Eq.10 as shown below:

where the TDA model θ TDA is private and inaccessible. Since this process represents a deterministic 712 mapping, the privacy leakage can be quantified as: 713

where H ( · ) denotes entropy and H ( X R i → S |X R i ) = 0 due to the nature of deterministic mapping. 714 Since X R i → S is derived from X R i through the private TDA model θ TDA , the amount of privacy 715 leakage can be further expressed as follows: 716

where | θ TDA | is the parameter space of the TDA model. As θ TDA aligns the distribution of X R i → S ) to the traffic domain of the target city through traffic domain alignment, reducing its correlation with the source city's traffic domain, H ( X R i → S | θ TDA ) takes on a small value, thereby minimizing the privacy leakage I ( X R i , X R i → S ) .

Privacy Protection in Traffic Secure Aggregation. Given the aggregated data X R→ S , the attacker aims to infer the transformer data X R i → S of the source city R i , where X ( R i → S ) ,R i is derived from X R i → S in Eq.16 as shown below:

̸

Since the traffic domains of source cities are aligned to that of the target city, they are from Independent 724 Identically Distributed (IID), and the privacy leakage can be quantified as: 725

̸

Since the above two processes is a Markov Chain [44], i.e., X R i → X R i → S → X R→ S , the total 726 amount of the privacy leakage can be bounded using the data processing inequality [56]: 727

This analysis demonstrates that the FedTT framework effectively minimizes privacy leakage by 728 leveraging both TDA and TSA, ensuring robust privacy protection in federated traffic knowledge 729 transfer. 730

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D Experimental Details

D.1 Baselines

Wecompare the FedTT framework with state-of-the-art baselines. First, we compare FedTT with three SOTA transfer methods in Federated Traffic Knowledge Transfer (FTT), including T-ISTGNN [49], pFedCTP [78], and 2MGTCN [75], as detailed below.

T-ISTGNN [49]. It designs a spatio-temporal GNN-based approach with an inductive mode for cross-region traffic prediction.

pFedCTP [78]. It designs an ST-Net for privacy-preserving and cross-city traffic prediction with personalized federated learning.

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2MGTCN [75]. It designs multi-modal GCNs and TCNs to capture spatial and temporal information and enhance adaptability across cities.

Besides, we compare FedTT with three SOTA transfer methods in Multi-Source Traffic Knowledge Transfer (MTT), including TPB [41], ST-GFSL [43], and DastNet [57], as detailed below.

TPB [41]. It utilizes a traffic patch encoder to create a traffic pattern bank for the cross-city few-shot traffic knowledge transfer.

ST-GFSL [43]. It transfers traffic knowledge through model parameter matching to retrieve similar spatio-temporal features.

DastNet [57]. It employs graph learning and domain adaptation to create domain-invariant node embeddings for the traffic data.

In addition, we compare FedTT with three SOTA transfer methods in Single-Source Traffic Knowledge Transfer (STT), including CityTrans [47], TransGTR [27], and MGAT [46], as detailed below.

CityTrans [47]. It proposes a domain adversarial model with knowledge transfer for spatiotemporal prediction across cities.

TransGTR [27]. It leverages adaptive spatio-temporal knowledge and domain-invariant features for TP in data-scarce cities.

MGAT [46]. It extracts multi-granular regional features from source cities to enhance the effectiveness of knowledge transfer.

Moreover, we extend three classic and SOTA centralized traffic models in FedTT and the existing two-stage transfer methods in FTT (referred as FTL), including Gated Recurrent Unit (GRU) [10], Convolutional Neural Network (CNN) [32], Multi-Layer Perceptron (MLP) [55], CityTrans [47], TransGTR [27], and MGAT [46], as detailed below.

ST-SSL [23]. It models traffic data at attribute and structure levels for spatial and temporal heterogeneous-aware traffic prediction.

DyHSL [80]. It leverages hypergraph structure information to extract dynamic and high-order relations of traffic road networks.

PDFormer [24]. It introduces self-attention and feature transformation for dynamic and flowdelay-aware traffic prediction.

To evaluate the Traffic View Imputation (TVI) method of FedTT in the ablation study, we replace this module with three SOTA data imputation methods, including LATC [8], GCASTN [48], and Nuhuo [73], as detailed below.

LATC [8]. It integrates temporal variation as a regularization term to accurately impute missing spatio-temporal traffic data.

GCASTN [48]. It uses self-supervised learning and a missing-aware attention mechanism to impute the missing traffic data.

Nuhuo [73]. It uses graph neural networks and self-supervised learning to accurately estimate missing traffic speed histograms.

D.2 Model Adaptability

Table 4 shows the overall performance when extending existing centralized traffic models (i.e., GRU [10], CNN [32], MLP [55], CityTrans [47], TransGTR [27], and MGAT [46]) in FTT using FedTT and FTL methods with MAE, where the best results are shown in blue. As observed, all centralized traffic models extended in FedTT achieve the best performance compared to those extended in FTL, also showing its effectiveness of traffic knowledge transfer in FTT, i.e., the gains range from 5.13% to 64.65% . Note that the DyHSL model has the best performance in centralized traffic models and is implemented in FedTT as the default model in other experiments.

D.3 Training Efficiency 785

Fig. 5 shows the communication size 786 (GB) and running time (minutes) of 787 different methods on traffic flow pre788 diction. As observed, the FedTT 789 framework has the least communica790 tion size and running time compared 791 to other methods, i.e., with communi792 cation overhead reduced by 90% and 793 running time reduced by 1 to 2 orders 794 of magnitude , showing its superior 795 efficiency of traffic knowledge trans796 fer in FTT. This is because FedTT se797 curely transmits and aggregates the 798

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traffic domain-transformed data using the TST module with relatively small computation and communication overheads, compared to other methods that employ the HE method for model secure aggregation in FTT. Besides, FedTT utilizes the FPT module to reduce data transmission and train models in parallel, significantly improving the training efficiency in FTT.

D.4 Parameter Sensitivity

Fig. 6 shows the performance of the FedTT framework with different hyperparameter settings (i.e., λ 1 and λ 2 ) on traffic flow prediction with MAE. First, the suggestion and optimum value of λ 1 is 0.7. As λ 1 increases, the generator model tends to generate the data that can "trick" the server discriminator model rather than generating the high-quality traffic domain transformed data, resulting in higher MAE. As λ 1 decreases, the server dis-

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criminator model loses its ability to effectively guide the generator model in generating traffic domain transformed data, resulting in higher MAE. Second, the suggestion and optimum value of λ 2 is 0.4. As λ 2 increases, the generator model tends to generate the data with a traffic domain that deviates significantly from that of the target city, resulting in higher MAE. As λ 2 decreases, the generator model generates the data with a more local-specific traffic pattern, which hinders the model from effectively learning the traffic patterns of the target city, resulting in higher MAE. Overall, FedTT has the best performance in all hyperparameter settings when λ 1 = 0 . 7 and λ 2 = 0 . 4 , which are used in FedTT as the default values in other experiments.

D.5 Case Study

To demonstrate the practical applicability of FedTT in real-world traffic knowledge transfer scenarios, we conduct a case study using the UTD19[42] dataset, which includes traffic data from 40 cities

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worldwide. For comparison, we select 2MGTCN, as it performs the best among the three existing methods in FTT (see Table 4.1). In this scenario, Groningen is chosen as the target city due to its limited traffic data and relatively sparse sensor deployment, making it challenging to train a highperformance traffic model independently. In contrast, London, Hamburg, Madrid, and Manchester are chosen as source cities because they possess significantly larger datasets and denser sensor networks, providing abundant traffic data for effective knowledge transfer. The statistics of these cities is summarized in Table 7. Since the sampling intervals of traffic data vary across cities, we resample all datasets in a uniform interval of 15 minutes to ensure that the temporal discrepancies between cities do not affect the model performance.

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NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

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It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

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The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

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If this information is not available online, the authors are encouraged to reach out to the asset's creators.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript titled \"Privacy-Preserving and Effective Cross-City Traffic Knowledge Transfer via Federated Learning\" proposes FedTT, a federated learning framework aimed at enabling privacy-preserving knowledge transfer for city-level traffic prediction tasks. While the application of federated learning in traffic modeling is timely and relevant, the manuscript presents several critical shortcomings in novelty.\n\nSTRENGTHS AND WEAKNESSES:\n1. The proposed approach lacks sufficient novelty. Similar frameworks and strategies have already been explored in recent literature within this domain.\n2. The introduction section does not adequately discuss the limitations of existing methods, nor does it clearly position how the proposed work addresses these gaps.\n3. Background information necessary to contextualize the work is missing or insufficient, making it difficult to assess the contribution in relation to prior art.\n4. The methodology is presented at a high level without sufficient technical depth or detailed explanations of critical components.\n5. The manuscript suffers from poor structural organization and weak presentation, which hinders readability and understanding.\n6. The evaluation uses a narrow set of metrics, which does not fully capture the performance or robustness of the proposed method.\n7. Visual elements, including figures, are poorly formatted and lack clarity—font sizes are small, structures are imprecise, and the visualizations are not sufficiently informative.\n\nQUESTIONS:\nN/A\n\nLIMITATIONS:\nThe manuscript does not sufficiently discuss the practical limitations of the proposed approach in real-world deployments. Can the authors elaborate on scenarios where FedTT may not perform well or where its applicability is restricted? Understanding these constraints is critical for assessing its utility in operational systems", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 1, "DATE": "2025-06-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FedTT, a framework designed for cross-city traffic knowledge transfer with privacy preservation. The paper introduces three key components: (i) TVI: missing traffic data in each city is completed using GAT and DyHSL; (ii) TDA: a GAN transforms source city data into the style of the target city to generate synthetic data; and (iii) TSA: synthetic data generated from source cities are transmitted to the target city and then averaged. Experiments are conducted on four real-world traffic datasets, comparing FedTT with many SOTA methods, along with ablation studies. The results show that FedTT outperforms these baselines in different aspects.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe paper is clearly written and well organized. One of its main strengths is the use of real-world traffic datasets. It considers a lot of SOTA methods, and the experiments are comprehensive, not only focusing on prediction error but also considering communication overhead, runtime, and privacy protection.\n\nWeaknesses (revised after rebuttal):\nThe clarity of the paper writing needs improvement, including the missing training processes for generators and discriminators in Algorithms 1 and 2, parameter settings such as the configured training rounds and corresponding communication rounds, as well as loss weights. How to ensure stable training of the generators and discriminators on clients and the discriminator on the server is also a challenge, especially when the number of clients increases.\n\nQUESTIONS:\n1. Please refer to the Weaknesses section and clarify how FedTT differs from other one-shot FL frameworks.\n\n2. I believe the paper should only be compared with methods that also transmit data, rather than model parameters. This is because comparing fundamentally different paradigms is unfair. Each comes with its own pros and cons. A major limitation of FedTT is that it does not yield a reusable global model; the trained model cannot be generalized to an unseen city. When facing a new city, FedTT would require re-collecting data and retraining the model from scratch.\n\n3. The necessity of TSA is questionable as it is executed on the server side. In FL, it is commonly assumed that the server is honest but cautious. The server could ignore the aggregation protocol and easily infer client data from Eq. (16). Furthermore, aggregation can reduce data volume, diversity, and city-specific granularity.\n\n4. The training procedure of the GAN is unclear. If communication between clients and the server is limited to data transfer, is the discriminator on the server the same model as the one used on the client side, or is it merely the same architecture trained independently? If the generator is trained on the client and the discriminator on the server, how is this adversarial training coordinated?\n\n5. FedTT assumes that the server (i.e., the target city) has access to 5% of its own training data and computation resources. Do all baseline methods assume access to the same amount of target city data and computational resources as FedTT?\n\nLIMITATIONS:\nFedTT lacks generalization to unseen cities.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents FedTT, a novel federated learning framework for cross-city traffic knowledge transfer that addresses challenges like privacy leakage, data distribution discrepancies, and low data quality. It introduces three key components: traffic view imputation to handle missing sensor data by capturing spatio-temporal dependencies, a traffic domain adapter to align traffic data across cities, and traffic secret aggregation to enhance privacy during training. The method is evaluated on real-world traffic datasets, showing improved performance in accuracy and efficiency compared to existing approaches while preserving data privacy.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper has the following strengths:\n\nS1. The paper makes a significant and novel contribution to the field of traffic prediction and federated learning through the introduction of FedTT, a privacy-preserving and effective framework for cross-city traffic knowledge transfer (Section 3). The proposed FedTT addresses and solves the limitations in existing FTT methods like privacy leakage, data distribution discrepancies, and low data quality. Prior methods like 2MGTCN, pFedCTP, and T-ISTGNN are used as baselines to demonstrate this distinction.\n\nS2. The paper presents three carefully designed components, i.e., TVI (Section 3.1), TDA (Section 3.2), and TSA (Section 3.3), each addressing a key challenge in Federated Traffic Knowledge Transfer (FTT). \n\nS3. The paper conducts extensive experiments to show the superiority of FedTT. The method is thoroughly evaluated on four real-world datasets, i.e., PeMSD4, PeMSD8, FT-AED, and HK-Traffic shown in Table 1, and compared with 14 state-of-the-art baselines, showing substantial improvements, i.e., up to 75.24% reduction in MAE and 67.54% in RMSE shown in Table 2. The privacy preservation evaluation also demonstrates FedTT’s superior resistance to reconstruction attacks, i.e., PCC<10% shown in Figure 6. A case study using the UTD19 dataset involving five global cities further validates the real-world applicability of FedTT shown in Figure 10.\n\nS4. The paper is generally well-written and logically organized, with clear motivation, methodology, and experimental analysis. Figures are informative and well-annotated, effectively conveying the workflow of the proposed modules. Tables are comprehensive and readable, with consistent metric reporting. The authors also provide detailed supplementary material, including theoretical proofs, training algorithms, complexity analysis, and implementation details, enhancing reproducibility and transparency.\n\nThe paper has the following weaknesses:\n\nW1. The paper uses a large number of symbols and definitions (e.g., Definitions 1--5, Eqs. 1--21), which may hinder readability, especially for readers not deeply familiar with the domain. Although a notation table is provided in Appendix B, it would be helpful to include this table or a condensed version in the main paper to improve accessibility and clarity.\n\nW2. The paper lacks sufficient detail in its experimental setup. Important training configurations such as learning rate, batch size, number of training epochs, and network bandwidth are missing and not mentioned. Additionally, other relevant setups like optimizer choice, early stopping criteria, and model initialization strategies, should be explicitly stated to ensure full reproducibility.\n\nW3. The paper presents rich experimental results (Table 2, Figs. 6–9), but the descriptions of the findings could be more detailed. In particular, explaining why FedTT outperforms certain baselines in different scenarios.\n\nW4. Tables 5 and 6 should be figures.\n\nQUESTIONS:\nQ1. Why can P^S and G^S in the TDA module be shared directly without considering privacy?\n\nQ2. In the Appendix D4 parameter sensitivity, FedTT does not seem to be sensitive to the hyperparameters \\lamda_1 and \\lamda_2. Is there a need to introduce these two hyperparameters?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-21"}

comments: SUMMARY:
The paper introduces FedTT, a federated learning framework designed for cross-city traffic knowledge transfer, which addresses challenges like privacy leakage, data discrepancies, and low data quality. FedTT incorporates three modules: 1) Traffic View Imputation (TVI) for imputing missing traffic data and capturing spatio-temporal dependencies; 2) raffic Domain Adapter (TDA) to transform traffic data uniformly across cities to address cross-city distribution differences; 3) Traffic Secret Aggregation (TSA) for privacy-preserving aggregation, maintaining security without reducing model performance.

STRENGTHS AND WEAKNESSES:
- Strengths:
1. FedTT introduces well-designed modules (TVI, TDA, TSA), which together systematically address the challenges of data quality, distribution discrepancies, and privacy.
2. Extensive experiments on real-world datasets showcase significant improvements (5.43%–75.24% MAE reduction) over existing baselines, validating the model’s effectiveness.
3. Theoretical privacy analysis and robust protection mechanisms ensure high resistance to data reconstruction attacks while greatly reducing communication overhead.

- Weaknesses:
1. The paper lacks detailed statistical error bars, confidence intervals, and deeper variance analyses in the results.
2. The impact of varying the number of source cities remains unexplored. This could influence results, especially for smaller or heterogeneous datasets.

QUESTIONS:
1. How does the performance scale when increasing or reducing the number of source cities (clients)?
2. Since the Traffic Domain Adapter (TDA) relies on adversarial training, did you observe stability issues during training?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02