From 22ae6e38a5aabbc678a52f15647c3672df077b0a Mon Sep 17 00:00:00 2001 From: Haobo Yang <55401837+yhbcode000@users.noreply.github.com> Date: Thu, 28 Nov 2024 18:19:57 +0400 Subject: [PATCH] Update README.md --- README.md | 78 ++++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 54 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index ab9d614..267d954 100644 --- a/README.md +++ b/README.md @@ -1,21 +1,24 @@ -# Model-SL Project +# Model-SL Project: Adaptive Split Learning with Localized Encryption and Decryption Models -**Model-SL** (Split Learning) is a research and development project focusing on implementing **Split Learning with Encryption** to enhance data security while leveraging **user history** for improved model performance. The project aims to address privacy concerns in collaborative machine learning by securely partitioning the learning process across multiple entities. +**Model-SL** is an innovative project that implements **Split Learning** with **adaptive encryption and decryption models**. These models are parameterized and run locally on user devices, leveraging **user history data** to train and fine-tune their parameters, enabling personalized and robust encryption-decryption mechanisms. This ensures data security and privacy while enhancing collaborative learning capabilities. + +**⚠️ Note: This project is under active construction. Expect frequent updates and experimental features.** --- ## Features -- **Split Learning**: Implements a partitioned neural network architecture where training is distributed between client and server without sharing raw data. -- **Encryption**: Adds robust encryption mechanisms to protect intermediate data exchanges. -- **User History Integration**: Leverages historical user data securely to improve model predictions and personalization. -- **Flexible Execution**: Supports both cloud-based and local execution. +- **Localized Encryption and Decryption Models**: Each user has locally parameterized encryption and decryption models, ensuring data never leaves their device unencrypted. +- **User History-Driven Training**: User history data is securely used to train and adapt the encryption and decryption models for personalized performance. +- **Split Learning**: The neural network is partitioned between the user and server, minimizing the exposure of sensitive data. +- **Privacy-Preserving Architecture**: The design ensures that raw user data remains secure and never directly shared with the server. +- **Modular Design**: Future-ready for adding new features and extending encryption mechanisms. --- ## Installation -To get started, clone this repository along with its submodules: +To get started, clone the repository with submodules: ```bash git clone --recurse-submodules https://github.com/AIerLab/model-sl.git @@ -27,7 +30,7 @@ git clone --recurse-submodules https://github.com/AIerLab/model-sl.git ### Cloud Execution -For cloud-based execution, run: +For deploying in a cloud environment: ```bash bash run_cloud.sh @@ -35,7 +38,7 @@ bash run_cloud.sh ### Local Execution -For local execution, run: +For testing locally on your machine: ```bash bash run_local.sh @@ -43,31 +46,54 @@ bash run_local.sh --- +## Current Workflow (Under Construction) + +1. **Data Partitioning**: + - User data is retained locally, and only encrypted representations are shared with the server. + +2. **Training Local Models**: + - **Encryption Model**: Trains on user history to generate secure representations of intermediate outputs. + - **Decryption Model**: Trains in tandem to decode the encrypted data on the server side. + +3. **Split Learning Integration**: + - The encrypted intermediate data is transmitted to the server for further processing, while local models adapt based on user-specific requirements. + +4. **Continuous Learning**: + - User models evolve by leveraging historical data, enhancing encryption robustness over time. + +--- + ## Folder Structure -- **`/model-sl/`**: Main repository containing the core codebase for split learning. -- **`/submodules/`**: Includes necessary submodules for encryption, user history management, and utility scripts. -- **`run_cloud.sh`**: Script for deploying and running the project on a cloud environment. -- **`run_local.sh`**: Script for running the project locally. +- **`/model-sl/`**: Main repository containing the split learning implementation. +- **`/submodules/`**: Includes experimental modules for encryption and decryption. +- **`/user-history/`**: Placeholder for user history data (ensure compliance with privacy standards). +- **`run_cloud.sh`**: Script for cloud execution. +- **`run_local.sh`**: Script for local execution. --- -## Prerequisites +## Development Status + +### Current Focus +- Implementing basic encryption and decryption models. +- Establishing secure communication protocols. +- Testing integration of user-specific data with model parameterization. -- Python 3.8+ -- Required libraries (install via `requirements.txt` if applicable) -- Access to cloud services (e.g., AWS, Azure, or GCP) for cloud execution -- Supported Linux environment for local execution +### Future Goals +- Fine-tuning encryption-decryption models using federated learning techniques. +- Enhancing performance through model optimization and hyperparameter tuning. +- Expanding support for additional use cases and datasets. --- -## Contributing +## Contribution -We welcome contributions to the **Model-SL Project**! To contribute: +As the project is still in its early stages, contributions are welcome! 1. Fork the repository. 2. Create a feature branch: `git checkout -b feature-name`. -3. Commit your changes: `git commit -m 'Add a new feature'`. +3. Commit your changes: `git commit -m 'Add new feature'`. 4. Push the branch: `git push origin feature-name`. 5. Open a pull request on GitHub. @@ -75,10 +101,14 @@ We welcome contributions to the **Model-SL Project**! To contribute: ## License -This project is licensed under the **MIT License**. See the `LICENSE` file for details. +This project is licensed under the **MIT License**. See the `LICENSE` file for more details. --- -## Support +## Support and Feedback + +For questions, feature requests, or bug reports, please reach out via [GitHub Issues](https://github.com/AIerLab/model-sl/issues) or email us at **support@aierlab.com**. + +--- -For issues and discussions, visit the [GitHub Issues](https://github.com/AIerLab/model-sl/issues) section or contact the team at **support@aierlab.com**. +**Stay tuned as we build the future of adaptive, secure split learning!**