Privacy-Preserving Collaborative Resilience: A Federated Foundation Time-Series and Multi-Agent Cooperative Scheduling Framework for Cross-Organizational Supply Chains

Abstract

The geometric escalation of geopolitical tensions and macro-economic volatility has brought the vulnerability of fragmented cross-organizational supply networks into sharp relief. Evaluating systemic resilience and executing multi-party collaborative dispatching remains heavily constrained by stringent data compliance mandates and the proprietary nature of corporate transactional data. To bridge this critical operational chasm, this paper explores an integrated paradigm that couples a federated foundation time-series architecture with a distributed multi-agent cooperative scheduling engine. Recognizing that traditional localized forecasting models fail to capture systemic structural shocks while centralized deep learning architectures fundamentally violate cross-border data privacy regulations, we leverage the zero-shot generalization capabilities of foundation time-series paradigms within a secure federated learning framework. This decentralized framework enables multi-tier supply chain participants to collaboratively train a global spatio-temporal predictive model without exposing raw transactional records. The empirical implementation of this coupled system, however, revealed non-trivial misalignments regarding non-independent and identically distributed data configurations and gradient communication synchronization bottlenecks. To accommodate these data-driven frictions, we construct a bi-level adaptive recourse mechanism where federated probabilistic intervals dynamically parameterize cross-organizational scheduling horizons. Our computational simulations on simulated multi-enterprise supply chain benchmarks suggest that the proposed architecture to some extent optimizes the delicate trade-off between privacy compliance costs and collaborative logistics efficiency under extreme disruption scenarios. Nevertheless, its performance stability remains bounded by localized data drifting tendencies, indicating that further research is needed to design resilient communication-efficient aggregation protocols.