To address the challenge of collaborative strategy optimization caused by non-independent and identically distributed data in cross-institutional scenarios, a federated quantitative learning framework integrating Path Attention Aggregation Module (PAAM), Gradient Clipping and Compression (GCC), and a Heterogeneity-Aware Adaptive Optimizer (HAAO) is proposed to achieve efficient return optimization and robust risk control. The framework is validated across multi-market and multi-institutional environments, with experiments covering three key dimensions: return performance, risk management, and communication efficiency. The results demonstrate that the proposed model achieves an annualized return (AR) of 16.57%, representing an approximate 19.7% improvement over the traditional FedAvg model; the Sharpe ratio (SR) increases to 1.25, while the maximum drawdown (MDD) decreases to 15.92% and volatility remains controlled at 8.83%, indicating superior balance between return and risk. In the communication efficiency evaluation, when the number of communication rounds is reduced to 50 and 25, the model maintains accuracy at 94.2% and 92.8%, recall at 93.5% and 91.7%, and precision at 94.8% and 92.3%, respectively. Overall, the proposed framework achieves a dynamic balance between global convergence and risk constraints through path weighting, gradient sparsification, and frequency-domain learning rate adjustment. This research provides a novel and scalable paradigm for distributed financial prediction that ensures both privacy preservation and communication efficiency, demonstrating substantial engineering feasibility and practical applicability in intelligent financial modeling.

Short-term cross-border prediction is of significant importance for global supply-chain risk management, international trade decision-making, and stability. However, existing approaches mainly rely on individual price sequences or limited structured variables, making it difficult to effectively perceive complex external factors, including cross-border trade flows, logistics state variations, exchange-rate fluctuations, and policy-driven shocks. To address these challenges, an artificial intelligence-driven sensing-oriented reliability- and consistency-aware multimodal framework for short-term cross-border prediction is proposed. Market conditions, exchange rates, cross-border trade activities, logistics operations, and news–policy events are jointly modeled as multisource intelligent sensing signals. In the proposed framework, the reliability and cross-modal consistency perception module is first developed to dynamically evaluate the credibility of different data sources and suppress the interference caused by missing, delayed, and conflicting information. Subsequently, the asynchronous cross-border temporal interaction module is introduced to capture the time-dependent propagation relationships among trade, logistics, exchange rates, and states. Furthermore, the dynamic trade and policy event perception module is constructed to identify short-term disturbances induced by tariff adjustments, trade restrictions, port disruptions, and major international events, thereby enabling intelligent prediction under complex cross-border environments. Based on a multisource cross-border sensing dataset constructed from January 2022 to December 2025, the performance of the proposed framework is systematically evaluated through three tasks, including short-term direction prediction, volatility forecasting, and risk level prediction. Experimental results demonstrate that the proposed method achieves an accuracy of 0.842, precision of 0.836, recall of 0.829, macro-F1 of 0.832, and AUC of 0.913 in the short-term direction prediction task, significantly outperforming ARIMA, XGBoost, LSTM, TCN, Transformer, PatchTST, and existing multimodal fusion models. Ablation studies further verify the critical contributions of reliability modeling, consistency constraints, asynchronous temporal interaction, and dynamic event perception modules to improving prediction performance.