Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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.
Abstract
Telecom operators hold some of the richest customer data in any industry, yet the models that would turn it into churn, lifetime-value, fraud, and personalization intelligence are held back by a structural problem: the data cannot be pooled. Customer records sit across regional operators, business units, edge networks, and clouds, each under a different privacy regime such as GDPR, CCPA, or telecom-specific governance, and moving them into one repository raises privacy risk, regulatory exposure, transfer cost, and latency. 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. The architecture engineers for the realities that break naive federation in telecom: non-independent, non-identically distributed data across regions, addressed with weighted aggregation, local epochs, adaptive learning rates, and client clustering; and heterogeneous, intermittently connected nodes, addressed with a hybrid-asynchronous coordination scheme, aggregation windows, fault-tolerant checkpointing, and client selection. Secure aggregation, differential privacy, and encrypted transport are bound to explicit GDPR, CCPA, and telecom-governance evidence so data locality is auditable, not merely asserted. We report projected performance ranges and an illustrative simulation rather than production benchmarks, and we state that limit plainly. The contribution is the reference architecture, its privacy-assurance model, a decision framework for when federation earns its complexity in telecom, and an evaluation plan for the empirical phase.
Training machine learning models across many institutions without moving raw data is attractive in theory, but the standard Federated Learning setup hands enormous power to one central aggregation server that participants must trust blindly. what happens when that trust assumption is violated and build a system, that does not require it. couples a four-layer distributed architecture with three stacked privacy mechanisms Local Differential Privacy to bound what any single gradient reveals, Secure Multi-Party Computation via the SecAgg protocol so the aggregation server never sees individual updates, and Tendermint-BFT blockchain consensus to create a tamper-evident audit log that no single party controls. Our main technical contribution is a formal proof that the combination of LDP and SecAgg satisfies end-to-end $(\varepsilon, \delta)$-differential privacy, including the SecAgg masking step cannot degrade the DP guarantee already earned by LDP noise injection. On CIFAR-10 with a ResNet-18 under three Dirichlet non-IID splits tested on real cluster hardware, reached 78.3% accuracy at $\varepsilon=0.83$ against six baselines including FedProx and SCAFFOLD-two standard non-IID benchmarks that prior blockchain FL work has consistently omitted.
Raman Dubey, A. Jain, Richa Sharma· International Conference on...· 0 citations
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.
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
Connected aftermarket devices extend vehicle diagnostics, repair workflows, and over-the-air software maintenance beyond original equipment manufacturer boundaries, yet their heterogeneous ownership and long service life complicate conventional perimeter security. This paper develops Zero Trust Federated Learning for Connected Aftermarket Devices (ZT FL CADE), an edge-learning architecture that combines device-level access control, privacy-preserving federated learning, and adversarial validation for over-the-air update and predictive maintenance decisions. The evaluation uses a single synthetic dataset of 144,000 telemetry windows from 240 devices, 12 vendor domains, and 180 days of operation. Features include bus entropy, update latency, attestation age, signature retries, environmental signals, fault-code rates, packet loss, drift, mileage, and trust score, with targets for maintenance risk, update intrusion, and access action. ZT FL CADE trains local temporal models, aggregates privacy-bounded updates, scores each device against behavioral and update integrity evidence, and routes update requests to allow, challenge, or quarantine actions. Synthetic experiments improve maintenance risk F1 from 0.837 for Fed Avg to 0.883, improve intrusion F1 from 0.856 to 0.897, and preserve 0.842 intrusion recall when 20 percent of selected clients are adversarial. Mean access-decision latency remains 44 MS, below the 100 MS operational budget used in the simulation. The results do not establish field validation, but they indicate that zero-trust policy enforcement and federated learning can be evaluated jointly rather than as separate aftermarket security controls. Index Terms Zero trust architecture, federated learning, connected aftermarket devices, over-the-air updates, adversarial machine learning, edge artificial intelligence, predictive maintenance, automotive cybersecurity
Federated learning allows banks, hospitals, and other regulated organizations to train a shared model without moving raw records off their own servers, which is attractive wherever data protection law or competitive sensitivity rules out pooling data centrally. Two problems limit how far this promise can be trusted in practice. First, the parameter updates that clients exchange still leak information about local records through gradient inversion and membership inference attacks. Second, an honest averaging rule such as FedAvg has no defense against a subset of clients that submit corrupted or adversarial updates, so a small number of malicious or compromised participants can quietly steer the shared model off course. This paper presents a federated learning framework, DP-BR-FedAvg, that combines a Gaussian-mechanism differential privacy layer with a coordinate-wise trimmed-mean Byzantine-robust aggregation rule, evaluated on a simulated cross-institutional classification task resembling fraud and clinical-risk scoring. Across sixty communication rounds with twenty clients, a quarter of them Byzantine, plain FedAvg collapses on the minority class (F1-score 0.030) while the proposed framework recovers substantially more of the signal (F1-score 0.119) while bounding the privacy loss of any single client's contribution. A Byzantine-robust aggregator with no privacy layer performs best in raw accuracy, quantifying the cost privacy imposes on robustness. The results show that privacy and robustness mechanisms interact rather than simply add, and that system design for regulated, adversarial, cross-institutional settings needs to budget for that interaction.
The deepening cyber-physical integration of smart grids has expanded the attack surface of power networks, while centralised intrusion detection schemes struggle with data silos, privacy exposure, and prohibitive communication costs across geographically dispersed substations. This paper proposes a federated deep learning framework that addresses these constraints jointly. A three-tier cloud-edge-terminal architecture confines raw measurements to local devices and exchanges only model parameters across tiers. At each edge node, a hybrid CNN-BiLSTM detector trained under focal loss captures both spatial protocol motifs and temporal attack signatures, including stealthy false data injection and APT traces. Privacy is preserved through a layer-selective mechanism that combines Paillier homomorphic encryption on sensitive gradient slices with calibrated differential privacy on the residual components, pushing the privacy-utility frontier outward without saturating cryptographic cost. An adaptive aggregation rule weights client updates by data quality, drift severity, and marginal validation contribution, mitigating the convergence pathologies that vanilla FedAvg exhibits under sharp non-IID partitioning. Experiments on NSL-KDD, CICIDS2017, and an ICS power-system corpus show that the proposed scheme recovers within 0.7 F1 points of the centralised upper bound, suppresses membership inference advantage to below 0.08, holds detection quality against up to 20% Byzantine clients, and converges in roughly half the rounds required by FedAvg. The framework offers a deployable path toward collaborative intrusion detection across regional grid operators without compromising data sovereignty.
Lin Chen, Zhuo Tang, Yiwei Yang et al.· Scientific Reports· 0 citations
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
