> For the complete documentation index, see [llms.txt](https://docs.2pm.network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.2pm.network/node-framework/federated-learning/typical-scenarios.md). # Typical Scenarios 2PM.Network leverages the strength of federated learning to address complex problems across industries by enabling collaborative model training without compromising data privacy, here's some if the typical scenarios: ### **Depin Data Utilization** This scenario focuses on using Depin data to detect faults and perform predictive maintenance. The approach enhances network security while preserving data privacy. This application is crucial for environments where data sensitivity and integrity are paramount, ensuring operational efficiency without exposing sensitive information. ### **Cross-Platform User Behavior Analysis** In this use case, we can develop marketing models and recommendation systems by integrating both Web2 and Web3 data. This scenario is aimed at businesses looking to leverage extensive data from various platforms while ensuring that user privacy is not compromised. It allows companies to deliver personalized experiences and efficient service offerings without risking user data exposure. ### **Anti-Money Laundering and Fraud Detection** This application performs joint anti-money laundering and fraud detection across institutions. It’s particularly beneficial for financial institutions that require a collaborative approach to prevent fraud and comply with regulatory requirements, safeguarding the privacy of all parties involved. By utilizing federated learning, institutions can share insights without directly sharing sensitive data, enhancing the collective ability to detect and prevent fraudulent activities. ### **LLM Joint Training** The scenario involves conducting joint training of a Large Language Model (LLM) using private data from multiple entities. This not only ensures data privacy but also boosts domain-specific performance. Such a setup is ideal for organizations that need to develop robust, industry-specific AI models without compromising the proprietary nature of their data. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.2pm.network/node-framework/federated-learning/typical-scenarios.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.