The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.
翻译:联合会学习的到来便利了机器学习模式之间在维护隐私的同时进行大规模的数据交换。尽管有短暂的历史,但联合会学习正在迅速发展,以更加实际地加以利用。该领域最重要的进步之一是将转移学习纳入联合会学习,这克服了联合会初级学习的基本限制,特别是安全方面的限制。本章从安全角度对联合会学习和转移学习的交叉性进行了全面调查。本研究的主要目标是发现可能损害联合会学习和转移学习系统隐私和功能的潜在弱点和防御机制。