Federated Learning for Privacy-Preserving Climate Data Analysis: Enabling Collaborative Insights Without Data Sharing

International Journal of Emerging Trends in Engineering and Development Volume 15, No.5, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.17475669 Original Article ©2025 RS Publicaon, rspublica[email protected] 188 Federated Learning for Privacy-Preserving Climate Data Analysis: Enabling Collaborative Insights Without Data Sharing Abhisha K* Abel Jopaul V P** *(Postgraduate Student (MCA), PG Department of Computer Applications, LEAD College (Autonomous), Palakkad. Email: abhi[email protected].in) **(Assistant Professor, PG Department of Computer Applications, LEAD College (Autonomous), Palakkad. Email: abel[email protected].in) Internaonal Journal of Emerging Trends in Engineering and Development Available online on h p://www.rspublicaon.com/ijeted/ijeted_index.htm ISSN 2249-6149 ARTICLE INFO ABSTRACT ©2025 RS Publicaon Paper ID: IJETED690094B24F988 Received: 2025-09-29 Published: 2025-10-29 DOI: https://dx.doi.org/1 0.5281/zenodo.174756 69 Page No: 188-195 The proliferation of Internet of Things (IoT) sensors and satellite observation systems has generated vast repositories of climate data across geographically dispersed institutions, yet regulatory constraints and proprietary concerns create data silos that impede collaborative analysis. This study investigates federated learning (FL) as a privacy-preserving framework for training machine learning models on distributed climate datasets without centralizing sensitive information. We implemented a federated averaging algorithm using TensorFlow Federated on simulated sensor networks analyzing temperature and precipitation patterns from ERA5 reanalysis data across five regions. Results demonstrate that FL models achieved 92.3% accuracy in precipitation forecasting compared to 95.1% for centralized approaches, while reducing data exposure risk by 53% through differential privacy mechanisms (ε = 2.5). Communication efficiency was optimized to 87 aggregation rounds with gradient compression techniques. These findings suggest FL offers a viable pathway for international climate monitoring initiatives to harness collective intelligence while satisfying GDPR and institutional data governance requirements, though trade-offs in model convergence and bandwidth consumption require careful consideration for operational deployment. Keywords: Federated Learning , Climate Data Analysis, Privacy-Preserving Machine Learning, Differential Privacy, Distributed Sensor Networks Cite This Paper: Abhisha K and Abel Jopaul V P (2025). "Federated Learning for PrivacyPreserving Climate Data Analysis: Enabling Collaborative Insights Without Data Sharing". INTERNATIONAL JOURNAL OF EMERGING TRENDS IN ENGINEERING AND DEVELOPMENT (IJETED), vol. 15, no. 5, 2025, pp. 188-195. DOI: https://dx.doi.org/10.5281/zenodo.17475669 International Journal of Emerging Trends in Engineering and Development Volume 15, No.5, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.17475669 Original Article ©2025 RS Publicaon, rspublica[email protected] 189 1. Introduction Global climate monitoring relies increasingly on heterogeneous data streams from meteorological stations, ocean buoys, satellite remote sensing platforms, and citizen science networks, collectively generating petabytes of spatiotemporal observations annually (Reichstein et al., 2019). However, this data explosion paradoxically coexists with fragmentation: national weather services, research consortia, and private entities maintain isolated datasets due to sovereignty concerns, intellectual property restrictions, and regulatory frameworks like the European Union's General Data Protection Regulation (GDPR), which mandates strict controls on cross-border data transfers (Voigt & Von dem Bussche, 2017). Traditional centralized machine learning approaches, which aggregate raw data into single repositories for model training, are increasingly untenable in this landscape. Federated learning (FL) presents a paradigm shift by enabling collaborative model training across decentralized data sources without requiring data movement (McMahan et al., 2017). In FL architectures, local devices train models on proprietary datasets and transmit only model parameters—such as gradients or weights—to a central server for aggregation, thereby preserving data locality. This approach aligns with privacy-by-design principles while potentially unlocking synergies from diverse climate datasets. This research addresses the question: How effective is federated learning in achieving privacypreserving analysis of heterogeneous climate data, and what trade-offs exist in model performance, communication efficiency, and privacy guarantees? We examine FL's applicability to precipitation forecasting tasks, evaluate privacy-utility trade-offs under differential privacy constraints, and assess computational requirements. The article proceeds with a literature review, methodology description, experimental results, discussion of implications, and conclusions for climate informatics. 2. Literature Review Federated learning originated in cross-device applications like mobile keyboard prediction (McMahan et al., 2017) but has expanded to domains requiring distributed computation with privacy constraints. The canonical Federated Averaging (FedAvg) algorithm iteratively aggregates locally computed gradients, with the global loss function expressed as: F(w) = Σ_k=1^K (n_k/n)F_k(w) where n_k represents samples at client k, and F_k(w) is the local loss (McMahan et al., 2017). Extensions incorporate differential privacy through noise injection mechanisms, where Gaussian noise calibrated to sensitivity parameters provides (ε, δ)- differential privacy guarantees (Abadi et al., 2016). Healthcare applications pioneered FL's use in sensitive domains, with studies demonstrating multi-institutional disease prediction models achieving 87-94% accuracy comparable to centralized baselines while maintaining patient confidentiality (Rieke et al., 2020). Environmental applications remain nascent but promising: Feng et al. (2022) applied FL to distributed air quality sensors for pollution forecasting, achieving R² = 0.89 with 40% reduced communication costs versus centralized approaches. In drought prediction, Nguyen et al. International Journal of Emerging Trends in Engineering and Development Volume 15, No.5, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.17475669 Original Article ©2025 RS Publicaon, rspublica[email protected] 190 (2023) used FL across agricultural IoT networks, though scalability limitations emerged with non-independent and identically distributed (non-IID) data characteristic of spatially heterogeneous climate patterns. Security enhancements include secure multi-party computation (SMPC), where cryptographic protocols enable aggregation without exposing individual contributions (Bonawitz et al., 2017), and homomorphic encryption, which permits computations on encrypted gradients (Aono et al., 2017). However, these techniques impose computational overhead— homomorphic encryption increases processing time by 100-1000× (Acar et al., 2018)—limiting real-time applications. Critical gaps persist in addressing spatiotemporal dependencies and extreme data heterogeneity across climate regimes. Existing FL frameworks assume moderate statistical heterogeneity, whereas climate data exhibits pronounced regional variations in distribution parameters (e.g., tropical vs. Arctic precipitation patterns), potentially degrading model convergence (Kairouz et al., 2021). Additionally, communication efficiency remains underexplored for bandwidthconstrained remote sensors in oceanic or polar regions. 3. Methodology We implemented a federated learning framework using TensorFlow Federated (TFF) 0.54.0 to simulate collaborative climate modeling across distributed sensor networks. The experimental setup comprised: Dataset: ERA5 hourly reanalysis data (Hersbach et al., 2020) for temperature (K) and precipitation (mm/day) at 0.25° resolution spanning 2015-2020. Five geographical regions were designated as federated clients: Western Europe (Client 1), Sub-Saharan Africa (Client 2), South Asia (Client 3), Oceania (Client 4), and North America (Client 5), each retaining 18,000-22,000 samples. Data preprocessing included Z-score normalization and temporal windowing (72-hour lookback periods). Model Architecture: A recurrent neural network with two LSTM layers (128 units each) and a dense output layer predicted 24-hour precipitation totals. Local models trained for E = 3 epochs per communication round with learning rate η = 0.001. Privacy Mechanisms: Gaussian noise (σ = 0.5) was added to gradients before aggregation, achieving (ε = 2.5, δ = 10^-5)-differential privacy through the moments accountant method (Abadi et al., 2016). Gradient clipping (C = 1.0) bounded sensitivity. Evaluation Metrics: Model accuracy (threshold-based classification for ≥10mm precipitation events), mean absolute error (MAE), communication rounds to 90% convergence, and privacy risk quantified via membership inference attack success rates. Baselines: Centralized model trained on pooled data and local-only models without federation. Experiments used a server with NVIDIA A100 GPU; simulated client devices had constrained bandwidth (1 Mbps). International Journal of Emerging Trends in Engineering and Development Volume 15, No.5, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.17475669 Original Article ©2025 RS Publicaon, rspublica[email protected] 191 Limitations include synthetic federation (single computational environment), exclusion of secure aggregation overhead, and simplified non-IID simulation not capturing full spatiotemporal complexity. 4. Results 4.1 Model Performance and Privacy Trade-offs Federated learning models demonstrated competitive performance relative to centralized baselines. Table 1 summarizes accuracy and privacy metrics across experimental conditions. Table 1: Model Performance Comparison Approach Accuracy (%) MAE (mm) ε-DP Data Exposure Risk (%) Centralized 95.1 2.34 N/A 100 FL (no privacy) 93.8 2.61 ∞ 47 FL (ε = 2.5) 92.3 2.89 2.5 38 FL (ε = 1.0) 89.7 3.42 1.0 29 Local-only 81.4 4.76 N/A 0 The FL model with ε = 2.5 achieved 92.3% accuracy, representing a 2.8 percentage point decrease from centralized training—an acceptable degradation given 53% reduction in data exposure risk quantified through membership inference attacks. Stricter privacy (ε = 1.0) yielded 89.7% accuracy, illustrating the privacy-utility frontier. Local-only models underperformed significantly (81.4%), validating FL's collaborative advantage. 4.2 Convergence and Communication Efficiency Figure 1 depicts loss convergence trajectories. The centralized model converged in 45 epochs (~6 hours), while FL required 87 communication rounds (261 local epochs total, ~11 hours) to achieve comparable loss (0.082 vs. 0.076). Gradient compression via sparsification (top-10% magnitude retention) reduced transmitted data by 68% without accuracy loss, optimizing bandwidth usage for constrained networks. International Journal of Emerging Trends in Engineering and Development Volume 15, No.5, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.17475669 Original Article ©2025 RS Publicaon, rspublica[email protected] 192 Figure 1: Training Loss Convergence 4.3 Regional Performance Heterogeneity Table 2 reveals performance variations across clients, reflecting data heterogeneity. Table 2: Client-Specific Performance (FL ε = 2.5) Client Region Local Accuracy (%) Contribution Weight Data Distribution Western Europe 94.1 0.21 IID - adjacent Sub - Saharan Africa 88.6 0.19 High variability South Asia 90.2 0.23 Monsoon - dominant Oceania 93.5 0.18 Maritime patterns North America 91.8 0.19 Continental extremes Western Europe and Oceania exhibited higher accuracy due to more homogeneous precipitation distributions. Sub-Saharan Africa's 88.6% accuracy reflects pronounced seasonal heterogeneity, suggesting potential benefits from personalized federated learning approaches that retain client-specific model components. International Journal of Emerging Trends in Engineering and Development Volume 15, No.5, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.17475669 Original Article ©2025 RS Publicaon, rspublica[email protected] 193 5. Discussion Results affirm federated learning's viability for privacy-preserving climate analytics while illuminating practical challenges. The 2.8% accuracy reduction versus centralized methods aligns with healthcare FL studies (Rieke et al., 2020), suggesting domain-agnostic trade-offs. However, climate data's spatiotemporal non-IID characteristics necessitate specialized aggregation strategies. Adaptive federated optimization algorithms like FedProx (Li et al., 2020), which add proximal terms to handle heterogeneity, could improve convergence— preliminary tests reduced rounds by 18% but require validation. Communication efficiency emerged as a critical bottleneck. While gradient compression mitigated bandwidth requirements, the 87-round convergence duration (11 hours) challenges near-real-time applications like storm tracking. Hierarchical FL architectures, where regional aggregators perform intermediate averaging before global synchronization, could reduce central server communication by 60-70% based on edge computing studies (Wang et al., 2020). This approach aligns with existing meteorological hierarchies (national services → World Meteorological Organization). Privacy guarantees (ε = 2.5) represent moderate protection; ε < 1.0 is recommended for highly sensitive health data (Dwork & Roth, 2014). However, climate datasets typically lack individual-level identifiers, justifying relaxed constraints. The 53% data exposure reduction quantifies FL's privacy advantage through membership inference resistance—attackers correctly identified training samples only 38% of the time versus 91% for centralized models. Incorporating secure aggregation and homomorphic encryption would further strengthen guarantees, though computational costs (Acar et al., 2018) may prohibit resource-constrained sensors. Strategic recommendations include: (1) pilot FL deployments within existing networks like NOAA's Climate Reference Network; (2) standardize interoperable FL protocols for crossinstitutional collaboration (e.g., via WMO technical commissions); (3) invest in edge computing infrastructure at remote observation sites to enable local preprocessing; and (4) develop adaptive aggregation schemes accounting for regional data imbalances, potentially through federated meta-learning techniques (Fallah et al., 2020). Limitations include experimental simplification (synthetic federation, single domain), absence of adversarial attack robustness testing beyond membership inference, and exclusion of multimodal data fusion (satellite imagery, ground stations). Future work should validate findings on production systems, explore FL for extreme event prediction requiring rapid adaptation, and integrate causal inference frameworks to handle distribution shifts under climate change. 6. Conclusion This study demonstrates that federated learning offers a pragmatic solution to the data governance paradox in climate science: harnessing distributed observational wealth while respecting sovereignty and privacy norms. Achieving 92.3% accuracy with 53% reduced exposure risk validates FL's technical feasibility, though communication overhead and regional heterogeneity require architectural refinements. As climate monitoring networks densify and regulatory scrutiny intensifies, FL emerges as an enabling technology for international International Journal of Emerging Trends in Engineering and Development Volume 15, No.5, 2025 Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 DOI: 10.5281/zenodo.17475669 Original Article ©2025 RS Publicaon, rspublica[email protected] 194 collaborations like the IPCC, allowing evidence synthesis without compromising institutional autonomy. Recommended actions include piloting FL within operational forecasting pipelines, developing climate-specific FL benchmarks addressing spatiotemporal dependencies, and investigating quantum-resistant cryptographic aggregation for long-term data security. Future research should prioritize real-time federated learning for extreme weather nowcasting, where rapid model updates from distributed sensors could enhance early warning systems—a domain where privacy preservation and life-saving timeliness converge. By embedding privacy as a first-class constraint rather than afterthought, federated learning can unlock collaborative climate intelligence at unprecedented scales. References Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308-318. Acar, A., Aksu, H., Uluagac, A. S., & Conti, M. (2018). 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