Federated Cross-Modal Dynamic Network Framework for Early Warning of Systemic Financial Risks via Complex Networks and Machine Learning

Abstract

This article proposes a dynamic network framework for federal collaboration to address the three major challenges of data fragmentation, model lag, and dynamic loss in current financial regulation. The aim is to achieve data fusion, sub second real-time response (single prediction<100ms), and auditable regulatory decision-making under cross institutional privacy protection; Methodologically, Yahoo Finance industry stock prices, FRED macro indicators, and IMF crisis annotation data were integrated. A three-layer overflow network of risk/volatility/return was constructed through LASSO-VAR variable screening and generalized variance decomposition, and a weighted synthetic network was used to capture the risk transmission path. At the same time, this paper designs a lightweight federated learning protocol and verifies its effectiveness against baselines including the logit model, random forest, XGBoost, SVM, BP neural network, and LSTM by inputting synthetic network metrics and 13 traditional metrics. Experimental results show that the dynamic network model achieves an AUC of 0.88 on the IMF dataset (an 11.4% improvement over the single-signal XGBoost model) and a crisis recall of 0.82 (a 26.2% improvement over the logit model). Furthermore, its prediction latency is reduced to 62.7 ms (meeting a high concurrency of 1590 queries per second), the convergence speed of federated training is increased by 1.8 times, and communication costs are reduced by 43%. In addition, in data contamination scenarios, the F1 decay is only 5.4%, and the response deviation to policy mutations is ±3.4%. Conclusions indicate that this framework, through dynamic network reconstruction and federated collaborative optimization, achieves an early warning accuracy of 0.923 and a crisis lead time of 32.6 days, providing an efficient solution for high-frequency financial risk control. Therefore, future extensions are possible to enhance the robustness of adversarial defenses.