2PM.Network
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  • Overview
    • What is 2PM.Network
    • Architecture
    • FAQ
    • Official Social Channels
  • 2PM Data VSIES Service
    • What is Data VSIES and why is it important
    • [V] Data Validation (ZK)
    • [SI] Data Standardization and Index
    • [E] Data Encryption Client (FHE)
    • [S] Data Storage and Access
    • Data VSIES SDK
  • Node Framework
    • Modular Architecture
    • Federated Learning
      • Horizontal Federated Learning Task
      • Logistic Regression Task
      • On-chain Secure Aggregation
      • Typical Scenarios
    • FHE Machine Learning
      • Built-in Models
      • Deep Learning
      • Typical Scenarios
    • Task Submission
    • Running a 2PM Node
      • Installation
      • Chain Connector Configuration
      • Data Preparation
      • Joining a Subnet
  • Security and Verification
    • Node Staking and Slash Mechanism
    • Running Verification Client
      • EigenLayer
      • Mind Network
    • Restaking and Delegation
  • Model Inference
    • 2PM Node Inference API
    • Posting Request to a Subnet Model
    • Getting Inference Results on Chain
      • Oracle Adapters
  • Monetization and Incentives
    • AI Model IP Assets
    • Distribution Algorithm
  • 2PM DAO
    • Build Subnets
      • Establishing New Subnets
      • General Requirements
      • Data Schema Definition
      • Model Selection
      • Task Implementation
    • $DMP Token
  • Deployed Smart Contracts
    • Subnets on Testnets
    • Local Deployment Guideline
  • Ecosystem
    • Partners
    • Use Cases
      • Private Personalized Recommendation
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On this page
  • Depin Data Utilization
  • Cross-Platform User Behavior Analysis
  • Anti-Money Laundering and Fraud Detection
  • LLM Joint Training
  1. Node Framework
  2. Federated Learning

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.

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Last updated 11 months ago