Federated Learning for Data-Driven Business Ecosystems: A Systematic Review of Scalable Distributed AI Architectures

This review synthesizes research on scalable federated learning architectures for data-driven business ecosystems to address challenges in scalability, governance, interoperability, security, and performance. The review aimed to taxonomize scalable architectures, evaluate governance and incentive mechanisms, benchmark interoperability solutions, identify privacy-preserving techniques, and synthesize performance trade-offs. A comprehensive literature selection spanning diverse domains and methodologies was analyzed using a structured framework focusing on architectural patterns, governance models, cross-domain collaboration, security, and efficiency. Findings reveal that hierarchical, decentralized, and blockchain-enabled architectures enhance scalability but often remain experimental with limited deployment in heterogeneous ecosystems. Governance frameworks incorporating reputation systems and automated incentives foster trust yet face challenges in strategic behavior and regulatory complexity. Interoperability advances enable multi-organizational collaboration, though data heterogeneity and legal constraints persist as barriers. Privacy-preserving methods, including differential privacy and secure aggregation, improve robustness but introduce computational overhead and accuracy trade-offs. Synthesizing these findings highlights the need for standardized benchmarks, mature governance models, and scalable architectures balancing efficiency, privacy, and compliance. These insights inform theoretical development and practical implementation of federated learning frameworks, guiding future research toward production-ready, interoperable, and secure solutions for complex business ecosystems.

کلیدواژه ها:

Federated Learning ، Data-Driven Business Ecosystems ، Scalability ، Governance ، Blockchain ، Privacy ، Interoperability