Causal-enhanced federated transfer learning framework for privacy-preserving cross-organizational strategic collaboration

Abstract

Cross-organizational strategic collaboration increasingly demands data-driven coordination while confronting stringent privacy constraints and heterogeneous data distributions. This paper proposes a coupled framework integrating causal inference with federated transfer learning to enable effective knowledge sharing across organizational boundaries without centralizing sensitive data. The framework comprises three core components: a distributed causal discovery algorithm that reconstructs inter-organizational causal structures through secure aggregation protocols, a causal-enhanced federated transfer learning mechanism that identifies invariant features transferable across heterogeneous enterprise contexts, and an adaptive privacy computing architecture that dynamically allocates protection resources according to data sensitivity profiles. Experimental validation across multi-enterprise ecosystems demonstrates that the causal enhancement mechanism improves cross-domain generalization robustness by 43% compared to conventional federated transfer learning, while maintaining predictive accuracy within 6.2% of centralized approaches. The adaptive privacy architecture retains 93.7% of collaborative utility under meaningful privacy constraints, with membership inference attack success rates near random baseline. These results establish that properly designed federated frameworks can approach centralized performance while preserving organizational autonomy, enabling broader participation in data-driven strategic alliances across regulatory and competitive boundaries.