AISWare AI² FL meets industry needs for cross-domain modeling and data sharing, providing secure, intuitive enterprise federated learning. It uses privacy computing and cryptography to ensure secure AI collaboration and compliant data operations across institutions.
Pre-configured, customizable scenario application templates designed for vertical industries, accelerating the swift implementation of federated learning technologies within these sectors.
Delivers secure, compliant data and intelligent services to industry clients, creating a new model for trustworthy data exchange.
Streamline complex operations into visual, automated processes, allowing users to focus on platform use and business, simplifying management and maintenance.
Automate and visualize operations for focused platform use and business, ensuring ready operational capability.
Secure and protect data throughout chains, enhance security and services for compliant data sharing.
Develop data availability and value, support diverse connectivity, boost interop, cut integration costs, foster ecosystem growth.
A car company, relying on AISWare AI² FL, utilizes its proprietary data along with operator's big data. Based on data standards and requirements, both parties select relevant characteristics of customers interested in trade-ins and repurchase. They output key tags and establish unilateral sub-models. Through the AISWare AI² FL platform, they collaboratively build models, including a model for assessing the intention to trade-in and repurchase, and a model for predicting the preferred models for such transactions, thereby enabling precise targeting of trade-in and repurchase opportunities.
A medical institution, in collaboration with an operator, has established a federated learning model architecture through AISWare AI² FL without the need to extract data from the database. This has virtually integrated operator data with medical data. They have created a federated recommendation model for expert consultation and rapid diagnosis, achieving precise marketing. This addresses the issue of recommending the same content to all users and realizes the goal of 'personalized experiences for each user' upon logging into the App, known as the 'thousand-person thousand-face' approach.
As data privacy regulations become increasingly stringent, data exchange between parties is restricted. Despite the fact that a certain operator has a vast array of user attribute and behavioral data—such as last month's spending, the number of calls made to 'XX Bank,' the number of contacts called, internet logs, historical searches, and app usage records—'XX Bank' struggles to access this information, leading to a phenomenon of data fragmentation. By relying on AISWare AI² FL, a bank and an operator, after connecting their respective datasets, use secure data alignment and vertical federated learning technology to perform vertical joint modeling on potential loan user models, all while meeting the requirements for data privacy and security.
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Tel:(010)82166688
Fax:(010)82166699
Business Cooperation:marketing@asiainfo.com
Complaints mailbox:AI_AC@asiainfo.com
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