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% 你只需要修改下面内容就可以完成中英文摘要，
% 这要求你具有一定的LaTeX基础，但是还是那句话，
% 如果你足够聪明，不具有LaTeX基础也可以完成。

% 中文摘要
\def\abstractzh{
%从这里开始写你的摘要，分段需要空一行。
从1955年达特茅斯会议开始，人工智能经过两起两落的发展，迎来了第三个高峰期。但是，在大多数行业中，数据是以孤岛的形式存在的，由于行业竞争、隐私安全、行政手续复杂等问题，即使是在同一个公司的不同部门之间实现数据整合也面临着重重阻力；另一方面，随着大数据的进一步发展，重视数据隐私和安全已经成为了世界性的趋势。

联邦学习作为一种切实可行的隐私保护手段，为多方机器学习建模提供了有效解决方案。这一隐私机器学习系统允许各方在确保本地数据隐私安全且符合法律法规的前提下，进行数据处理和模型构建。本文通过实验证明了联邦学习在实际应用中的有效性和优越性。联邦学习不仅在传统机器学习技术的基础上提供了隐私计算，为用户带来强大的隐私保护机制，而且在保证模型精度的同时实现了更优的性能。

本研究成功地运用金融信贷场景的数据，完成了基于FATE框架的联邦学习风险预测模型的设计、训练与实现，并将其与单机构数据训练的模型进行了对比。分析结果表明，借助联邦学习，金融机构之间的合作将能得到极大的推动力。

同时，本文通过对不同数据分布下联邦学习训练结果的对比分析，成功测试了数据分布对联邦训练效果的影响。研究结论显示，在总数据量不变的情况下，数据分布越均匀，联邦学习模型训练效果越佳。在现实商业活动中，各合作方应当充分参考各自数据集的分布特点，以便更深刻地评估联邦学习背后的商业价值。

在大数据安全的背景下，联邦学习为解决数据安全、数据泄露、用户隐私等问题提供了一种高效的解决方案。但在某些方面仍存在提升空间，如计算成本较高等。因此，在未来的研究中，需要进一步探索更加安全且高效的隐私计算技术，将其融入联邦学习框架中，为解决现实应用中的问题提供更为强大的支持。
%摘要结束
}

% 中文关键字 
% TODO: 改成可变长度的
\def\abszhkeyone{联邦学习}
\def\abszhkeytwo{FATE}
\def\abszhkeythree{金融信贷}
\def\abszhkeyfour{风险预测}
\def\abszhkeyfive{机器学习}

% ABSTRACT
\def\abstracten{
%Your abstract here, to make a new paragraph, give an extra blank line please.
Since the 1955 Dartmouth Conference, artificial intelligence has gone through two ups and downs and ushered in its third peak. However, in most industries, data exists in the form of isolated islands. Due to industry competition, privacy security, complex administrative procedures, and other issues, it faces significant challenges to integrate data even within different departments of the same company. On the other hand, with the further development of big data, the focus on data privacy and security has become a global trend.

Federated learning as a practical privacy protection measure provides an effective solution for multi-party machine learning modeling. This privacy-preserving machine learning system allows multiple parties to process data and develop models while ensuring local data privacy and security in compliance with laws and regulations. This paper demonstrates the effectiveness and superiority of federated learning in practical applications through experiments. Federated learning not only provides privacy-preserving computation based on traditional machine learning technology but also has better performance while ensuring model accuracy.

This study successfully applied data from financial scenarios to the FATE framework for the design, training, and implementation of federated learning risk prediction models and compared them with models trained on single-entity data. The analysis results show that with the help of federated learning, collaboration between financial institutions can yield a significant boost.

At the same time, this paper analyzes the impact of data distribution on federated training results through a comparison of different data distribution settings, successfully testing the effects of federated learning under various data distributions. Research conclusions show that with a constant total data volume, the more uniform the data distribution, the better the training results of federated learning models. In real-world business activities, all parties should fully consider the distribution characteristics of their datasets to more profoundly assess the commercial value behind federated learning.

In the context of big data security, federated learning provides an efficient solution for issues such as data security, data leakage, and user privacy. However, there is still room for improvement in some aspects, such as high computational costs. Therefore, future research needs to further explore more secure and efficient privacy computing technologies, integrate them into the federated learning framework, and provide more robust support for solving practical application problems. 
%Abstract done
}

% Key Words 
% TODO: 改成可变长度的
\def\absenkeyone{Federated Learning}
\def\absenkeytwo{FATE}
\def\absenkeythree{Financial Credit}
\def\absenkeyfour{Risk Prediction}
\def\absenkeyfive{Machine Learning}


