标题： 使用优化的同态加密提高联合学习的效率 显示英文标题

标题： Improving Efficiency in Federated Learning with Optimized Homomorphic Encryption

摘要： 联邦学习是一种在机器学习中使用的方案，允许多个设备在不共享私人数据的情况下一起协作训练模型。 每个参与者将其私人数据保留在本地系统中，并训练一个本地模型，仅向中央服务器发送更新信息，中央服务器将这些更新信息合并以改进总体模型。 联邦学习中隐私的关键推动因素是同态加密（HE）。 同态加密允许直接对加密数据执行计算。 尽管同态加密提供了强大的隐私保障，但它计算密集度高，导致显著的延迟和可扩展性问题，特别是在像BERT这样大规模的模型中。 在我的研究中，我旨在解决这个效率低下的问题。 我的研究引入了一种新颖的算法来解决这些低效问题，同时保持强大的隐私保障。 我在算法中集成了多种数学技术，如选择性参数加密、敏感性图和差分隐私噪声，这已经提高了其效率。 我还进行了严格的数学证明，以验证方法的正确性和鲁棒性。 我通过用C++编码实现了该算法，在大规模模型的联邦学习环境中模拟了环境，并验证了我所提出算法的效率比最先进的方法提高了$3$倍。 这项研究对机器学习具有重要意义，因为它能够在平衡隐私的同时提高效率，使其成为一种实用的解决方案！ 它将使联邦学习能够非常高效地使用，并部署到各种资源受限的环境中，因为这项研究为联邦学习的一个关键挑战——同态加密的低效问题提供了一个新颖的解决方案：我的新算法能够在保持强大隐私保障的同时增强联邦学习的可扩展性和资源效率。 显示英文摘要

摘要： Federated learning is a method used in machine learning to allow multiple devices to work together on a model without sharing their private data. Each participant keeps their private data on their system and trains a local model and only sends updates to a central server, which combines these updates to improve the overall model. A key enabler of privacy in FL is homomorphic encryption (HE). HE allows computations to be performed directly on encrypted data. While HE offers strong privacy guarantees, it is computationally intensive, leading to significant latency and scalability issues, particularly for large-scale models like BERT. In my research, I aimed to address this inefficiency problem. My research introduces a novel algorithm to address these inefficiencies while maintaining robust privacy guarantees. I integrated several mathematical techniques such as selective parameter encryption, sensitivity maps, and differential privacy noise within my algorithms, which has already improved its efficiency. I have also conducted rigorous mathematical proofs to validate the correctness and robustness of the approach. I implemented this algorithm by coding it in C++, simulating the environment of federated learning on large-scale models, and verified that the efficiency of my algorithm is $3$ times the efficiency of the state-of-the-art method. This research has significant implications for machine learning because its ability to improve efficiency while balancing privacy makes it a practical solution! It would enable federated learning to be used very efficiently and deployed in various resource-constrained environments, as this research provides a novel solution to one of the key challenges in federated learning: the inefficiency of homomorphic encryption, as my new algorithm is able to enhance the scalability and resource efficiency of FL while maintaining robust privacy guarantees.