With the increasing demand for privacy preservation and strategy sharing in global financial markets, traditional centralized modeling approaches have become inadequate for multi-institutional collaborative tasks, particularly under the realistic challenges of multi-source heterogeneity and non-independent and identically distributed (non-IID) data. To address these limitations, a heterogeneity-aware Federated Quantitative Learning framework, Federated Quantitative Learning, is proposed to enable efficient cross-market financial strategy modeling while preserving data privacy. This framework integrates a Path Quality-Aware Aggregation Mechanism, a Gradient Clipping and Compression Module, and a Heterogeneity-Adaptive Optimizer, collectively enhancing model robustness and generalization. Empirical studies conducted on multiple real-world financial datasets, including those from the United States, European Union, and Asia-Pacific markets, demonstrate that Federated Quantitative Learning outperforms existing mainstream methods in key performance indicators such as annualized return, Sharpe ratio, maximum drawdown, and volatility. Under the full model configuration, Federated Quantitative Learning achieves an annualized return of 12.72%, a Sharpe ratio of 1.12, a maximum drawdown limited to 10.3%, and a reduced volatility of 9.7%, showing significant improvements over methods such as Federated Averaging, Federated Proximal Optimization, and Model-Contrastive Federated Learning. Moreover, module ablation studies and attention mechanism comparisons further validate the effectiveness of each core component in enhancing model performance. This study introduces a novel paradigm for secure strategy sharing and high-quality modeling in multi-institutional quantitative systems, offering practical feasibility and broad applicability.

Under the growing demand for processing multimodal and cross-lingual information, traditional retrieval systems have encountered substantial limitations when handling heterogeneous inputs such as images, textual layouts, and multilingual language expressions. To address these challenges, a unified retrieval framework has been proposed, which integrates visual features from images, layout-aware optical character recognition (OCR) text, and bilingual semantic representations in Chinese and English. This framework aims to construct a shared semantic embedding space that mitigates semantic discrepancies across modalities and resolves inconsistencies in cross-lingual mappings. The architecture incorporates three main components: a visual encoder, a structure-aware OCR module, and a multilingual Transformer. Furthermore, a joint contrastive learning loss has been introduced to enhance alignment across both modalities and languages. The proposed method has been evaluated on three core tasks: a single-modality retrieval task from image → OCR, a cross-lingual retrieval task between Chinese and English, and a joint multimodal retrieval task involving image, OCR, and language inputs. Experimental results demonstrate that, in the joint multimodal setting, the proposed model achieved a Precision@10 of 0.693, Recall@10 of 0.684, nDCG@10 of 0.672, and F1@10 of 0.685, substantially outperforming established baselines such as CLIP, LayoutLMv3, and UNITER. Ablation studies revealed that removing either the structure-aware OCR module or the cross-lingual alignment mechanism resulted in a decrease in mean reciprocal rank (MRR) to 0.561, thereby confirming the critical role of these components in reinforcing semantic consistency across modalities. This study highlights the powerful potential of large language models in multimodal semantic fusion and retrieval tasks, providing robust solutions for large-scale semantic understanding and application scenarios in multilingual and multimodal contexts.