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<h2>Introduction</h2>
<p>Urban digital twins (UDTs) represent a paradigm shift in city management, providing dynamic, data-driven replicas of physical urban environments that enable real-time monitoring, simulation, and optimization (Park & Kim, 2022). By integrating data from Internet of Things (IoT) sensors, traffic cameras, energy grids, and citizen mobile devices, UDTs promise to enhance sustainability, resilience, and quality of life. However, the centralization of sensitive data in traditional UDT architectures raises profound privacy and security concerns, as data breaches or misuse can expose personal information, behavioral patterns, and critical infrastructure vulnerabilities (Gupta et al., 2020).</p><p>Federated learning (FL) has emerged as a promising distributed machine learning paradigm that addresses these privacy challenges by enabling collaborative model training across decentralized data sources without requiring raw data to leave local devices (Treleaven et al., 2022; Bonawitz et al., 2021). In FL, only model updates (e.g., gradients) are shared with a central server, which aggregates them to improve a global model. This approach inherently reduces the attack surface for data leakage. When combined with additional privacy-preserving techniques such as differential privacy (DP) and secure multi-party computation (SMC), FL can provide formal privacy guarantees against inference attacks (Chamikara et al., 2021; Iqbal et al., 2023).</p><p>The application of FL to UDTs is still nascent but holds great potential. UDTs operate in highly heterogeneous environments with non-independent and identically distributed (non-IID) data, varying communication bandwidth, and diverse stakeholder trust assumptions (Wang et al., 2023). Existing FL frameworks have been successfully deployed in domains such as healthcare (Rahman et al., 2022; Sahid et al., 2024), intrusion detection (Alazab et al., 2023), and noise mapping (Kumar, 2022), but their adaptation to the multi-modal, real-time requirements of UDTs remains underexplored.</p><p>In this article, we propose a comprehensive FL framework for privacy-preserving UDTs that integrates differential privacy, secure aggregation, and Byzantine-robust aggregation to address the unique challenges of urban data ecosystems. We evaluate the framework through extensive simulations using a realistic smart city dataset. Our contributions include: (1) a modular architecture for FL in UDTs that decouples data owners, edge nodes, and the aggregation server; (2) a formal privacy analysis combining local DP with secure aggregation; (3) empirical results demonstrating the trade-off between privacy and utility; and (4) guidelines for deployment in real-world smart city initiatives.</p>
<h2>Literature Review</h2>
<p>Federated learning has been extensively studied as a privacy-preserving machine learning technique. Śmietanka et al. (2020) provided an early overview of FL for privacy-preserving data access, highlighting its potential in financial and healthcare sectors. Bonawitz et al. (2021) formalized the security guarantees of FL protocols, including secure aggregation and differential privacy. Chamikara et al. (2021) proposed a distributed machine learning framework that combines FL with differential privacy to protect against gradient leakage.</p><p>Several studies have extended FL to specific application domains. Kumar (2022) introduced FL-NoiseMap, a federated learning-based privacy-preserving urban noise pollution measurement system, demonstrating that FL can effectively aggregate noise data from distributed sensors while preserving individual privacy. Alazab et al. (2023) applied FL to intrusion detection in IoT networks, showing improved detection rates with lower privacy risk. In healthcare, Rahman et al. (2022) surveyed FL-based AI approaches for smart healthcare, and Sahid et al. (2024) applied FL to Alzheimer’s disease classification using MRI data, achieving high accuracy without sharing patient data.</p><p>Privacy-preserving techniques in FL include differential privacy (Ahmadzai & Nguyen, 2023; Iqbal et al., 2023), secure aggregation (Gao et al., 2023), and blockchain integration (MengJuan & Jiang, 2022; Li et al., 2021). Zhou (2024) proposed a privacy-preserving Byzantine-robust FL method that resists malicious updates while maintaining privacy. Zhang et al. (2022) addressed collusion resistance in mobile crowdsensing. Mahmood and Jusas (2022) introduced a blockchain-enabled multi-layered security FL platform. These works collectively demonstrate the maturity of FL privacy mechanisms.</p><p>Urban digital twins have been discussed in the context of 6G and metaverse (Alwis et al., 2021; Chang et al., 2022; Yang et al., 2022). However, the integration of FL into UDTs is less explored. Existing UDT architectures often rely on centralized data lakes, which pose privacy risks. Our work bridges this gap by proposing a dedicated FL framework for UDTs that leverages the latest advances in privacy-preserving FL.</p>
<h2>Methodology</h2>
<h4>Framework Architecture</h4><p>We propose a three-tier FL architecture for UDTs: (1) <em>Data Owners</em> (e.g., municipal sensors, citizen devices) that generate local data; (2) <em>Edge Nodes</em> that perform local model training and apply differential privacy; and (3) a <em>Central Aggregator</em> that performs secure aggregation and updates the global model. Each edge node collects data from a subset of data owners within its geographic zone, reflecting the non-IID nature of urban data. Communication between tiers uses encrypted channels.</p><h4>Privacy Mechanisms</h4><p>We employ local differential privacy (LDP) at each edge node by adding Gaussian noise to model updates before transmission, with privacy budget ε = 1.0 per round (Iqbal et al., 2023). Secure aggregation is implemented using the protocol of Bonawitz et al. (2021), ensuring that the aggregator only sees the sum of updates. Additionally, we incorporate a Byzantine-robust aggregation rule (trimmed mean) to mitigate malicious updates (Zhou, 2024).</p><h4>Dataset and Simulation</h4><p>We simulate a smart city with 50 edge nodes, each covering a distinct neighborhood. The dataset comprises three modalities: traffic flow (vehicles per hour), energy consumption (kWh per household), and noise levels (dB). Data are generated using realistic distributions based on public datasets from CityPulse and UCI repositories. We simulate 10,000 data points per node, split 80/20 for training and testing. The global model is a multi-task neural network with shared hidden layers and task-specific output heads. We train for 100 communication rounds with a batch size of 32 and learning rate 0.01.</p><h4>Evaluation Metrics</h4><p>We measure: (1) <em>Model accuracy</em> (mean absolute error for regression tasks); (2) <em>Privacy leakage</em> via membership inference attack (MIA) success rate (Shakeer & Babu, 2024); (3) <em>Communication overhead</em> (total bytes transmitted); and (4) <em>Convergence speed</em> (rounds to reach target loss). We compare against a centralized baseline where all data are pooled.</p>
<h2>Results</h2>
<p>Table 1 presents descriptive statistics of the simulated dataset across the three modalities. The data exhibit significant heterogeneity across nodes, with coefficients of variation exceeding 30% for all modalities, confirming the non-IID nature.</p><figure class="table-figure"><table><thead><tr><th>Modality</th><th>Mean</th><th>Std Dev</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Traffic (veh/h)</td><td>1250.4</td><td>412.3</td><td>210.0</td><td>2890.0</td></tr><tr><td>Energy (kWh)</td><td>18.7</td><td>6.2</td><td>2.1</td><td>45.6</td></tr><tr><td>Noise (dB)</td><td>65.3</td><td>8.9</td><td>42.0</td><td>92.0</td></tr></tbody></table><figcaption>Table 1. Descriptive statistics of simulated urban dataset (aggregated across all nodes).</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/federated-learning-for-privacy-preserving-urban-digital-twins-a-framework-and-empirical-evaluation-wm9em/figure-1-1779797833499.octet-stream" alt="bar chart comparing mean absolute error (MAE) across modalities for FL vs centralized model" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. bar chart comparing mean absolute error (MAE) across modalities for FL vs centralized model</figcaption></figure></p><p>Figure 1 illustrates the MAE comparison. The FL model achieves MAE of 0.112 (traffic), 0.094 (energy), and 0.087 (noise) on the test set, while the centralized model achieves 0.108, 0.091, and 0.084 respectively. The average degradation is 2.3%, indicating that FL preserves utility effectively.</p><p>Table 2 shows the privacy leakage assessment via MIA success rate. The FL framework with LDP and secure aggregation reduces MIA success from 72% (centralized) to 8%, a 90% reduction.</p><figure class="table-figure"><table><thead><tr><th>Scenario</th><th>MIA Success Rate (%)</th><th>Privacy Budget ε</th></tr></thead><tbody><tr><td>Centralized (no privacy)</td><td>72.0</td><td>N/A</td></tr><tr><td>FL without DP</td><td>35.4</td><td>∞</td></tr><tr><td>FL with LDP (ε=1.0)</td><td>8.2</td><td>1.0</td></tr><tr><td>FL with LDP + Secure Agg.</td><td>7.9</td><td>1.0</td></tr></tbody></table><figcaption>Table 2. Membership inference attack success rate for different privacy configurations.</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/federated-learning-for-privacy-preserving-urban-digital-twins-a-framework-and-empirical-evaluation-wm9em/figure-2-1779797849873.octet-stream" alt="line graph showing convergence curves (loss vs communication rounds) for FL and centralized training" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. line graph showing convergence curves (loss vs communication rounds) for FL and centralized training</figcaption></figure></p><p>Figure 2 shows that FL converges in approximately 60 rounds, close to the centralized baseline (50 rounds). Communication overhead per round is 2.1 MB for FL versus 500 MB for centralized (due to raw data transfer), representing a 99.6% reduction.</p><p>Table 3 reports regression coefficients from a linear model fitted to the FL accuracy as a function of number of nodes and privacy budget. Both factors significantly affect accuracy, with privacy budget having a larger effect.</p><figure class="table-figure"><table><thead><tr><th>Variable</th><th>Coefficient</th><th>Std. Error</th><th>t-value</th><th>p-value</th></tr></thead><tbody><tr><td>Intercept</td><td>0.145</td><td>0.012</td><td>12.08</td><td><0.001</td></tr><tr><td>Number of Nodes (per 10)</td><td>-0.003</td><td>0.001</td><td>-3.00</td><td>0.003</td></tr><tr><td>Privacy Budget ε (per unit)</td><td>0.018</td><td>0.004</td><td>4.50</td><td><0.001</td></tr></tbody></table><figcaption>Table 3. Linear regression coefficients for FL accuracy (MAE) as a function of number of nodes and privacy budget.</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/federated-learning-for-privacy-preserving-urban-digital-twins-a-framework-and-empirical-evaluation-wm9em/figure-3-1779797868405.octet-stream" alt="heatmap of accuracy across nodes, showing non-IID impact" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 3. heatmap of accuracy across nodes, showing non-IID impact</figcaption></figure></p>
<h2>Discussion</h2>
<p>Our results demonstrate that FL can achieve near-centralized performance in UDT scenarios while drastically reducing privacy risks. The 2.3% accuracy degradation is acceptable for many urban applications, especially when weighed against the 90% reduction in MIA success. This aligns with findings from Kumar (2022) in noise mapping and Alazab et al. (2023) in intrusion detection.</p><p>The convergence speed of FL is comparable to centralized training, indicating that communication overhead is not a bottleneck in our simulation. However, real-world deployments may face bandwidth constraints, which can be mitigated by techniques such as gradient compression (Wang et al., 2023). The non-IID data distribution, reflected in the negative coefficient for number of nodes in Table 3, suggests that more nodes increase heterogeneity and slightly degrade accuracy. This can be addressed through personalized FL or clustering (Rahman et al., 2022).</p><p>Privacy budget ε=1.0 provides strong protection, but stricter budgets (e.g., ε=0.1) may further reduce utility. Practitioners must balance privacy and accuracy based on regulatory requirements (e.g., GDPR). The combination of LDP and secure aggregation provides defense-in-depth, as secure aggregation prevents the server from seeing individual updates, while LDP protects against inference even if the server is compromised (Bonawitz et al., 2021; Gao et al., 2023).</p><p>Our framework is extensible to other urban domains, such as smart farming (Gupta et al., 2020) and vehicular networks (MengJuan & Jiang, 2022). Integration with blockchain could provide auditability and incentive mechanisms (Li et al., 2021). The emergence of 6G networks will further enable low-latency FL (Chang et al., 2022).</p><p>Limitations include the use of simulated data; real-world validation is needed. Also, we assumed honest-but-curious adversaries; malicious attacks (e.g., Byzantine) require additional robustness (Zhou, 2024). Future work should explore adaptive privacy budgets and cross-silo FL for city-scale deployment.</p>
<h2>Conclusion</h2>
<p>This article presented a federated learning framework for privacy-preserving urban digital twins, integrating differential privacy and secure aggregation. Empirical evaluation on a simulated smart city dataset showed that FL achieves accuracy within 2.3% of centralized training while reducing membership inference attack success by 90%. The framework is communication-efficient and converges quickly, making it suitable for real-time urban applications. We provided practical insights into the privacy-utility trade-off and highlighted directions for future research, including real-world pilots, integration with blockchain, and adaptive privacy mechanisms. Federated learning offers a viable path toward trustworthy urban digital twins that respect citizen privacy while enabling data-driven city management.</p>
<h2>References</h2>
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