Aug 2026· Information· Vol 17, pp. 811· 0 citations· 32 references
TL;DR
This paper investigates a privacy-preserving approach to churn prediction that combines federated learning (FL) with differential privacy (DP), and highlights the potential of privacy-preserving federated learning for practical distributed analytics applications where protecting sensitive data is essential.
Abstract
Predicting customer churn in the telecom sector is critical for retaining subscribers, maintaining brand reputation, and staying ahead of competitors. Losing customers not only reduces revenue but can also weaken long-term market position in a highly competitive industry. While machine learning has been widely used to address this challenge, most traditional approaches depend on centralizing customer data. This raises major concerns about user privacy, data ownership, and compliance with strict regulations such as GDPR. These challenges make it difficult for businesses to fully utilize customer data while safeguarding sensitive information. In this paper, we investigate a privacy-preserving approach to churn prediction that combines federated learning (FL) with differential privacy (DP). Rather than collecting all customer data in a single repository, the investigated framework enables multiple clients to collaboratively train a deep neural network while maintaining data locality during the federated training process. To further enhance privacy protection, we employ Differentially Private Stochastic Gradient Descent (DP-SGD) and add controlled noise to model updates, reducing the possibility of inferring individual data contributions. This work systematically evaluates how different privacy levels, expressed through ε and δ, influence model performance under simulated non-IID client distributions. The experiments analyze the privacy–utility trade-off using multiple evaluation metrics and compare the results with centralized and non-private federated-learning approaches. The findings show that the investigated framework maintains competitive predictive performance across a range of privacy budgets while demonstrating a clear privacy–utility trade-off. Very strict privacy budgets result in substantial performance degradation, particularly for smaller and more imbalanced datasets, whereas moderate privacy budgets maintain competitive predictive performance with limited degradation. This study highlights the potential of privacy-preserving federated learning for practical distributed analytics applications where protecting sensitive data is essential.
This paper presents a privacy-by-design federated learning reference architecture for customer intelligence across telecom networks, in which each operator trains locally on its own CRM, billing, service-interaction, and network-telemetry data and shares only encrypted model updates, never raw records.
Brahmananda Naidu Dabbara· International journal of com...· 0 citations
Cryptocurrency fraud encompassing pump-and-dump schemes, Ponzi contracts, phishing, ransomware laundering, and exchange manipulation inflicted estimated losses of USD 9.9 billion globally in 2023. Existing centralised fraud detection systems require pooling sensitive transactional data across financial institutions and blockchain analytics firms, posing severe privacy, regulatory, and competitive risks. This paper introduces FedHDL (Federated Heterogeneous Deep Learning), a novel privacy-preserving framework for cryptocurrency fraud detection that enables collaborative model training across five heterogeneous institutional nodes without raw data exchange. FedHDL integrates a Graph Attention Network (GAT) for transaction graph encoding, a Bidirectional LSTM (BiLSTM) for temporal behavioural modelling, and a Transformer-based attention fusion layer to reconcile divergent feature spaces across nodes with non-IID (non-independent and identically distributed) data distributions. Model aggregation employs a novel Reputation-Weighted Federated Averaging (RW-FedAvg) algorithm resistant to Byzantine gradient attacks. Privacy is enforced through the synergistic application of Rényi Differential Privacy (RDP) with a Gaussian mechanism (ε = 2.0, δ = 10⁻⁵) and additive homomorphic encryption of gradient updates. Evaluated on 276.23 million real-world and synthetic cryptocurrency transactions spanning five institutional nodes, FedHDL achieves an F1-score of 94.4%, AUC-ROC of 0.981, and accuracy of 96.4% surpassing the best competing federated baseline (FedProx+LSTM) by 7.7 percentage points in F1 and approaching centralised oracle performance (F1 = 95.1%) within 0.7 percentage points, while preserving strict data locality. Communication overhead is reduced by 89.7% relative to uncompressed FedAvg through gradient sparsification and top-k compression. These results demonstrate that FedHDL constitutes a practical, privacy-compliant, and high-fidelity solution for cross-institutional cryptocurrency fraud detection.
Kanika Singhal, Deepak Chandra Uprety, Bhawna Singh et al.· Journal of Intelligent Decis...· 0 citations
Gradient-boosted trees outperform neural networks on tabular data without privacy and off-path privatization and sequential noise accumulation explain the behavior; boosting’s main advantage is not accuracy but communication, achieving one to three orders of magnitude fewer values per client.
The results demonstrate that federated learning is a scalable and effective method that can achieve privacy compliance in e-commerce analytics within data-restricted environments, and it lays a solid foundation for secure distributed business intelligence.
Jing Hao· International Conference on...· 0 citations
Federated learning is a decentralised machine-learning approach in which several clients jointly build a shared model without moving their raw data to one location. Rising concerns around privacy, tightening regulation, and restrictions on how data may be owned or shared have made this approach increasingly attractive in practice. Although federated learning lowers privacy exposure relative to centralised training, deploying it in practice is complicated by clients whose data are unevenly distributed and non-identically distributed, by clients that participate inconsistently, and by training that can converge unpredictably. To obtain global models that train reliably and consistently even when client data are heterogeneous, this work puts forward a federated learning system built around privacy preservation. The design follows a client–server pattern in which a coordinating server aggregates updates from local models using weights that account for imbalance among participants. The behaviour of the resulting system is examined methodically across several data-distribution regimes — IID, mildly non-IID, and severely non-IID. The experiments show that the framework converges reliably and delivers predictive accuracy that holds up well, especially in the more difficult non-IID cases. Compared with conventional federated learning baselines, the approach shows greater robustness and steadier performance across successive training rounds. Because it is simple to implement, repeatable, and built with real deployment in mind, the architecture suits privacy-sensitive, decentralised use cases such as distributed intelligent systems, industrial monitoring, and healthcare analytics.
Shyam Patel, S. Khan· 2026 International Conferenc...· 0 citations
Federated learning allows financial institutions to collaboratively identify fraud without distributing raw transaction data, while differential privacy safeguards individual records from inference attacks. Utilizing a lightweight four-layer neural network that was trained on a 10,000-sample subset of the PaySim mobile money dataset (originally 6.3 million transactions), this paper provides a systematic empirical evaluation of a differentially private federated fraud detection framework. The framework is evaluated in the context of varying data distributions (IID and multiple non-IID schemes), clients' numbers, participation rates, privacy budgets, and data quantity skews. The findings indicate that IID and moderately heterogeneous settings produce high accuracy and F1-scores. Conversely, performance is substantially undermined by severe non-IID partitions, numerous small clients, and extreme data imbalances, particularly when differential privacy noise is implemented. The non-private model's detection capability is largely preserved by intermediate privacy budgets (approximately ε≈1). Notably, membership inference attack success rates remain at or near random guessing (approximately 50% accuracy) in the absence of differential privacy, indicating that the baseline provides a restricted attack surface. In this evaluation setting, the privacy benefit is further confirmed by the fact that differential privacy at ε≈1 further suppresses the attack advantage toward zero. While these results provide practical configuration advice for federated, privacy-preserving fraud detection systems, they should be interpreted within the context of the simplified experimental setup that was implemented.
Gian Maxmillian Firdaus, M. Abdurohman, B. Erfianto et al.· International Conference on...· 0 citations
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