Core Insights - The Chinese federated learning industry is experiencing steady growth driven by policy support, technological advancements, and market demand, with a projected market size of 254 million yuan in 2024, representing a year-on-year increase of 11.89% [1][8] - Federated learning effectively addresses the challenges of data silos and privacy security, enhancing model accuracy by over 20% in various applications such as financial risk control, medical joint diagnosis, and urban traffic optimization [1][8] Industry Overview - Federated learning (FL) is a distributed machine learning method aimed at enabling collaborative model training while protecting data privacy. It allows participants to train models locally using their own data and share encrypted model parameters with a central server, thus avoiding data sharing across institutions and complying with privacy regulations like GDPR [2] - The industry has evolved through four stages since the concept was introduced by Google in 2017: exploration, application, ecosystem building, and mature expansion [3] Market Size - The market size of the Chinese federated learning industry is expected to reach 254 million yuan in 2024, with a growth rate of 11.89% year-on-year [1][8] - The industry is supported by continuous iterations of technological frameworks, such as WeBank's FATE and Ant Group's shared intelligence platform, which incorporate privacy protection technologies like homomorphic encryption and secure multi-party computation [1][8] Key Companies - Leading companies in the federated learning sector include Ant Group and WeBank, with Ant Group holding a 36.7% market share in the privacy computing market for three consecutive years [8] - WeBank has pioneered the application of federated learning technology in the financial sector, with its open-source FATE framework becoming an industry benchmark [8] Industry Development Trends - The integration of federated learning with AI large models, edge computing, and 5G/6G technologies is expected to create a new paradigm of distributed AI collaboration [10] - Applications of federated learning are expanding beyond finance and healthcare into industrial internet, autonomous driving, and energy management, enhancing the technology's role in digital transformation [11][12] - The establishment of standards and the improvement of domestic policies are expected to strengthen the industry's foundation, with initiatives like the IEEE P3652.1 standard and the implementation of data security laws providing compliance support [13]