Federated transfer learning under heterogeneous data
Abstract
Recent advancements in artificial intelligence (AI) applications rely on massive amounts of training data. In practice, these valuable data are independently distributed among multiple data owners (e.g., companies and individuals), whose quantities are typically modest, and the data are usually heterogeneous. Collecting data from individual users or acquiring data from data owners is a conventionally popular and straightforward solution to this issue. However, such solutions have become obsolete due to the rising trend of data privacy and data security concerns. Currently, AI systems face the problem of utilizing fragmented and diverse data that are independently distributed across several data owners. Federated learning (FL), a novel privacy-preserving collaborative machine learning paradigm, is proposed to address the privately isolated small data learning problem. Its main idea is to compose a federation of data owners in which all participants virtually assemble their data without sacrificing data security and privacy. There are several challenges for federated learning, including communication efficiency, data security and privacy protection, and statistical learning. Among these challenges, the statistical learning challenge caused by heterogeneous data significantly affects the performance of FL systems and thus prohibits FL’s applications in practice. In recent years, academics have developed a machine learning paradigm known as transfer learning, which utilizes…
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