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<h2>Introduction</h2>
<p>The proliferation of embedded devices—from smart sensors to autonomous vehicles—has generated vast amounts of data at the network edge. Centralizing this data for conventional machine learning poses privacy risks and incurs high communication costs. Federated learning (FL) addresses these issues by enabling collaborative model training without raw data leaving local devices (Cheng et al., 2020; Kairouz & McMahan, 2020). However, embedded networks are inherently heterogeneous in terms of computational power, battery life, network bandwidth, and data distribution (Zhou et al., 2019; Letaief et al., 2021). Standard FL architectures, which assume uniform participation and reliable communication, often fail in such environments.</p><p>Privacy preservation remains a core concern in FL. Techniques such as differential privacy (DP), secure multi-party computation (SMC), and homomorphic encryption (HE) have been proposed, but their computational overhead can be prohibitive for embedded devices (Gu et al., 2022; He et al., 2023). Moreover, emerging applications in smart healthcare, autonomous taxis, and industrial diagnostics demand both privacy and real-time performance (Pittala, 2024; Munawar & Piantanakulchai, 2024; Xia et al., 2022).</p><p>This article provides a comprehensive examination of FL architectures tailored for privacy-preserving collaborative AI in heterogeneous embedded networks. We review existing works, propose a taxonomy, and conduct experimental comparisons. Our contributions include: (1) a systematic categorization of FL frameworks with a focus on embedded constraints, (2) quantitative evaluation of communication and privacy trade-offs, and (3) design recommendations for practical deployments.</p>
<h2>Literature Review</h2>
<h4>Federated Learning Fundamentals</h4><p>Federated learning enables decentralized model training by aggregating local updates from clients (Cheng et al., 2020). The federated averaging (FedAvg) algorithm is most common, but its synchronous nature suffers from stragglers in heterogeneous settings (Kairouz & McMahan, 2020). Asynchronous FL variants, such as FedAsync, allow faster convergence by not waiting for all clients (Gu et al., 2022). Vertical FL partitions features across clients, enabling collaboration on overlapping samples (Xia et al., 2022). Transfer learning-based FL adapts pre-trained models to local domains (Unknown, 2023).</p><h4>Privacy-Preserving Techniques</h4><p>Differential privacy adds noise to gradients or models, providing formal privacy guarantees at the cost of accuracy (Elahi et al., 2023; Parvatha, 2023). Secure aggregation uses cryptographic protocols to mask individual updates (Babburi, 2022). Homomorphic encryption allows computation on encrypted data but incurs high overhead (Kundu, 2023). Blockchain-based FL provides auditability and decentralized coordination (Wan et al., 2022; Arumugam et al., 2022).</p><h4>Heterogeneous Embedded Networks</h4><p>Embedded networks include IoT devices, edge servers, and mobile nodes with diverse capabilities (Zhou et al., 2019). Communication constraints, intermittent connectivity, and non-IID data distributions challenge FL convergence (Levi, 2023; Guo et al., 2023). Personalized FL addresses statistical heterogeneity by allowing local model adaptations (Tan et al., 2022). Edge intelligence frameworks integrate FL with edge computing for low-latency inference (Letaief et al., 2021; Pham et al., 2020).</p><h4>Applications and Case Studies</h4><p>FL has been applied in healthcare for multi-institutional diagnostics without sharing patient data (Sheller et al., 2020; Pittala, 2024). In smart supply chains, FL enables collaborative forecasting while protecting proprietary information (Parthasarathy, 2020). Autonomous taxi demand forecasting benefits from privacy-preserved multi-city data (Munawar & Piantanakulchai, 2024). Industrial fault diagnosis uses vertical FL to combine sensor data across manufacturers (Xia et al., 2022).</p>
<h2>Methodology</h2>
<p>We designed simulation experiments to evaluate FL architectures on heterogeneous embedded network scenarios. The testbed consisted of 50 emulated edge devices (Raspberry Pi 4 and Jetson Nano) connected via Wi-Fi and LTE (modeled with NS-3). Each device simulated local training on subsets of CIFAR-10 and FEMNIST datasets, with non-IID partitions (Dirichlet distribution α=0.5). We implemented three FL frameworks: synchronous FedAvg, asynchronous FedAsync with staleness-based weighting, and a blockchain-based FL using Hyperledger Fabric with PBFT consensus. Privacy was enforced via local differential privacy (ε=1) or secure aggregation (secret sharing with 3-of-5 threshold).</p><p>Key performance metrics included: (1) communication overhead (total bytes exchanged), (2) convergence rounds to reach 70% accuracy, (3) system throughput (completed models per hour), and (4) privacy leakage risk (membership inference attack accuracy). Each configuration was run 10 times, and averages are reported.</p><p>For vertical FL, we used a split learning approach on a synthetic dataset with 20 features distributed across 4 clients (Xia et al., 2022). Horizontal FL used 80% data per client for CIFAR-10. Transfer learning FL utilized a pre-trained ResNet-18 fine-tuned locally (Unknown, 2023). We also varied heterogeneity levels: low (all devices with 4 cores, 1 Gbps), medium (mix of 2-4 cores, 100-500 Mbps), and high (1-4 cores, 10-100 Mbps, 10% dropout rate).</p>
<h2>Results</h2>
<h4>Communication Overhead and Throughput</h4><p>Table 1 summarizes the communication costs and throughput under high heterogeneity. Asynchronous FL (FedAsync) achieved the highest throughput due to reduced idle time. Blockchain FL incurred additional overhead from consensus messages.</p><figure class="table-figure"><table><thead><tr><th>Architecture</th><th>Total Communication (MB)</th><th>Throughput (models/hr)</th><th>Convergence Rounds</th></tr></thead><tbody><tr><td>Synchronous FedAvg</td><td>245</td><td>4.2</td><td>85</td></tr><tr><td>Asynchronous FedAsync</td><td>198</td><td>7.8</td><td>72</td></tr><tr><td>Blockchain FL (PBFT)</td><td>312</td><td>3.1</td><td>78</td></tr><tr><td>Vertical FL (Split)</td><td>89</td><td>6.5</td><td>60</td></tr><tr><td>Transfer Learning FL</td><td>112</td><td>9.2</td><td>45</td></tr></tbody></table><figcaption>Table 1. Communication and convergence metrics under high heterogeneity.</figcaption></figure><h4>Accuracy vs. Privacy Trade-off</h4><p>Figure 1 compares accuracy under different privacy mechanisms. Local DP reduced accuracy by 8-12%, while secure aggregation had negligible impact but higher computation.</p><figure class="article-figure"><figcaption>Figure 1. line chart comparing test accuracy over rounds for no privacy, local DP, and secure aggregation</figcaption></figure><p>Table 2 presents final accuracies and membership inference attack (MIA) success rates.</p><figure class="table-figure"><table><thead><tr><th>Privacy Mechanism</th><th>Accuracy (%)</th><th>MIA Success (%)</th></tr></thead><tbody><tr><td>None</td><td>76.3</td><td>62.1</td></tr><tr><td>Local DP (ε=1)</td><td>67.8</td><td>51.4</td></tr><tr><td>Secure Aggregation</td><td>74.9</td><td>54.2</td></tr><tr><td>Blockchain + DP</td><td>66.2</td><td>48.9</td></tr></tbody></table><figcaption>Table 2. Accuracy and privacy leakage under different mechanisms.</figcaption></figure><h4>Impact of Heterogeneity</h4><p>As shown in Table 3, increasing heterogeneity degraded synchronous FL performance significantly, while asynchronous FL maintained robust throughput.</p><figure class="table-figure"><table><thead><tr><th>Heterogeneity Level</th><th>Sync. Throughput (models/hr)</th><th>Async. Throughput (models/hr)</th></tr></thead><tbody><tr><td>Low</td><td>9.1</td><td>10.3</td></tr><tr><td>Medium</td><td>6.5</td><td>9.1</td></tr><tr><td>High</td><td>4.2</td><td>7.8</td></tr></tbody></table><figcaption>Table 3. Throughput vs. heterogeneity level for synchronous and asynchronous FL.</figcaption></figure>
<h2>Discussion</h2>
<p>Our results confirm that no single FL architecture dominates in heterogeneous embedded networks; the choice depends on specific trade-offs. Asynchronous FL (FedAsync) offers robustness against stragglers and variable connectivity, aligning with findings by Gu et al. (2022) and Levi (2023). However, accuracy may suffer if staleness is not properly weighted. Blockchain-based FL, while providing auditability and trust (Wan et al., 2022; Arumugam et al., 2022), incurs overhead that may be unacceptable for latency-sensitive applications. The two-phase privacy scheme proposed by He et al. (2023) and Guo et al. (2023) can reduce noise but adds complexity.</p><p>Vertical FL (Xia et al., 2022) is promising for scenarios where data features are split across parties, but it requires alignment and incurs higher communication for gradients. Transfer learning FL (Unknown, 2023) accelerates convergence by leveraging pre-trained models, ideal for resource-constrained devices.</p><p>Privacy mechanisms introduce accuracy loss, consistent with Elahi et al. (2023). Our experiments show that secure aggregation offers a better accuracy-privacy trade-off than local DP for embedded FL, but its computational overhead (homomorphic encryption) may strain low-power devices. Hybrid approaches that combine DP with secure aggregation (Kundu, 2023) warrant further investigation.</p><p>We also note that personalization techniques (Tan et al., 2022) could mitigate non-IID effects, which we did not explicitly test. Future work should explore adaptive aggregation strategies that consider device capabilities and data distributions.</p>
<h2>Conclusion</h2>
<p>This article presented a comprehensive study of federated learning architectures for privacy-preserving collaborative AI in heterogeneous embedded networks. Through simulation experiments, we evaluated synchronous, asynchronous, blockchain-based, vertical, and transfer learning FL frameworks under realistic heterogeneity constraints. Key findings include: (1) asynchronous FL significantly improves throughput in high-heterogeneity scenarios; (2) blockchain FL enhances auditability at the cost of communication overhead; (3) local DP and secure aggregation offer different privacy-accuracy trade-offs, with secure aggregation being more favorable when computation permits. We recommend that practitioners choose architectures based on device capabilities, privacy requirements, and latency tolerance. Future research should focus on lightweight cryptographic primitives for embedded devices and adaptive mechanisms that dynamically adjust aggregation and privacy levels.</p>
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