Aug 2026· International Journal of Computational and Experimental Science and Engineering· 0 citations· 23 references
TL;DR
The paper proposes a systematic analysis framework for investigating five structural aspects, which need behavioral-specific adjustment beyond regular federated learning approaches in the following contexts: feature engineering with data locality; communication efficiency during distributed behavioral model training; differential privacy in behavioral prediction pipelines; non-IID distribution of behaviors in cross-silo federations; and secure aggregation with Byzantine resilience.
Abstract
Predictive customer behavioral modeling has long assumed centralized access to raw interaction data an assumption that regulatory frameworks, competitive constraints, and cross-organizational data governance requirements render increasingly untenable in contemporary enterprise environments. Federated learning offers a principled alternative, enabling collaborative model training across distributed data holders without centralizing behavioral records. However, standard federated learning frameworks were designed for device-level settings whose structural properties differ substantially from those of customer behavioral data: interaction sequences are longer and sparser, distributions across organizational participants are more heterogeneous, and privacy sensitivities are more legally consequential. The paper proposes a systematic analysis framework for investigating five structural aspects, which need behavioral-specific adjustment beyond regular federated learning approaches in the following contexts: feature engineering with data locality; communication efficiency during distributed behavioral model training; differential privacy in behavioral prediction pipelines; non-IID distribution of behaviors in cross-silo federations; and secure aggregation with Byzantine resilience. For each dimension, the article identifies the specific failure modes that arise when canonical methods are applied without adaptation and provides practitioner-oriented design guidance. The analysis further proposes a deployment-prioritized research agenda whose sequencing is determined by the severity with which unresolved challenges block production deployment. The framework contributes both a diagnostic lens for organizations evaluating federated behavioral intelligence adoption and a structured research roadmap for the methods community.
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
A privacy-aware Federated Data Engineering framework that integrates federated learning, distributed data engineering, and secure model aggregation for cross-enterprise analytics and incorporates privacy-enhancing technologies to ensure confidentiality, integrity, and transparency is presented.
Karen Spärck Jones, Donald Michie· International Journal of Dat...· 0 citations
Modern organizational analytics rely on enterprise data warehouses (EDWs). However, large-scale and centralized AI-driven mining of sensitive data stored in these warehouses means that an organization is vulnerable to privacy leaks, inference attacks, and not complying with regulations. Many of the current privacy-preserving frameworks are not designed for direct integration with OLAP environments. As a result, they cannot provide both high analytical utility and formal privacy protection. The framework proposed in this paper provides a definitive solution through integrating OLAP-based multidimensional feature engineering and federated learning (FL) through FedAvg and differentially private stochastic gradient descent (DP-SGD). The system is designed to partition EDW data across multiple logical clients and allows for collaborative training of a single global neural network while keeping all raw data stored locally within each client. Accuracy of classification results for the UCI Adult Income public benchmark and a synthetic EDW-based dataset tested against four baseline models are reported to be 94.8% with a ε of 2.0 for the synthetic EDW dataset, which is only 1.4% lower than centralized. Furthermore, the use of warehouse-aware federated analytics methods has been shown to reduce membership inference attack (MIA) success rates from 79.4% to 54.8%. Finally, the overall system was maintained with a formal (ε, δ)-differential privacy guarantee, thus validating the solution as being scalable, compliant with regulations, and suitable for the development of privacy-preserving enterprise AI.
Sangeetha S. B., T. C.· International Journal of Dat...· 0 citations
Cross-institutional collaboration in English learner corpus construction promises richer, more representative datasets but faces persistent barriers rooted in data privacy, regulatory compliance, and institutional reluctance to share sensitive learner writing samples. This paper introduces AdaDP-FedSec, a federated learning framework that enables multiple institutions to jointly train corpus-based language models without exchanging raw data. The framework incorporates three integrated mechanisms: an adaptive privacy budget allocation strategy that dynamically calibrates differential privacy noise based on gradient variance and institutional data characteristics, a hybrid secure aggregation protocol combining Shamir secret sharing with Paillier homomorphic encryption to prevent server-side gradient inspection, and a contribution-aware weighted aggregation scheme coupled with a dual-layer personalized model architecture to address cross-institutional data heterogeneity. Experiments conducted across eight simulated institutional nodes on grammatical error detection and writing proficiency classification tasks demonstrate that AdaDP-FedSec recovers roughly three-quarters of the performance gap between standard differentially private federated learning and centralized training, while pushing membership inference attack success close to chance levels. The adaptive budgeting mechanism emerges as the most impactful component, yielding 3–5% point improvements over uniform noise allocation at matched total privacy expenditure. Taken together, these findings point toward a workable—if still early—pathway for privacy-preserving collaborative corpus training in educational NLP.
Xi Zhou, Chunmei Yuan· Scientific Reports· 0 citations
As enterprise data grows across cloud, edge, and geographically distributed environments, traditional centralized analytics face challenges related to privacy, security, scalability, and regulatory compliance. To address these issues, this study proposes an Adaptive Federated Analytics Framework (AFAF) for distributed enterprise data systems. The framework enables collaborative analytics without sharing raw data by incorporating adaptive node selection, dynamic aggregation, privacy-preserving mechanisms, and communication optimization techniques. Unlike conventional federated approaches, AFAF dynamically evaluates node reliability, computational capacity, data quality, and network conditions to improve analytical performance. The framework integrates differential privacy, secure multi-party computation (SMPC), and encrypted aggregation to ensure enterprise-grade security and compliance. Experimental evaluations in hybrid cloud enterprise environments demonstrate improvements in analytical accuracy, aggregation efficiency, communication overhead, convergence stability, scalability, and fault tolerance compared with traditional federated systems. The framework also supports real-time analytics through adaptive participation thresholds and aggregation frequencies, enabling timely insights from distributed data sources. Results indicate that AFAF provides a scalable, secure, and efficient platform for privacy-preserving enterprise intelligence, with future enhancements including AI-driven orchestration, blockchain-based trust management, and autonomous analytics optimization.
K. R., B. S. Shah· International Journal of App...· 0 citations
A comprehensive survey of federated prompt learning (FPL) is presented to review recent advances in integrating the federated learning paradigm and large language models, while discussing security, privacy, and robustness issues and summarizing existing defense mechanisms.
Qinglin Yang, Chen Qiu, Hongyu Zhang et al.· 0 citations
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