[OTHER]：Add Federated Learning (FL) - Horizontal Federated Logistic Regression Implementation

[Yxfgghhh]

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Description

I would like to contribute a lightweight and optimized implementation of Horizontal Federated Logistic Regression (2025 optimized version) to this project. This implementation is tailored for privacy-preserving machine learning scenarios, which is a cutting-edge topic in modern ML/AI research and industrial applications.

Background & Motivation

Federated Learning (FL) enables multiple parties to collaboratively train a machine learning model without sharing raw data, effectively addressing critical data privacy challenges (e.g., compliance with GDPR, CCPA, and local data protection regulations). The proposed implementation focuses on horizontal federated logistic regression, which is suitable for scenarios where multiple parties hold datasets with identical features but disjoint samples—such as credit risk assessment across banks and user behavior classification across IoT devices. It integrates state-of-the-art optimizations for practical deployment:

• Asynchronous gradient aggregation: Eliminates synchronization bottlenecks in traditional synchronous FL frameworks, supporting heterogeneous devices with varying computing capabilities.
• Differential privacy: Implements ε-differential privacy (ε=1.0) via Gaussian noise injection to prevent sensitive data leakage from local gradients.
• Adaptive learning rate: Dynamically adjusts the learning rate during training to accelerate model convergence and avoid late-stage oscillation.
• Lightweight design: Pure C++17 implementation that only depends on the standard library, with no external ML frameworks (e.g., TensorFlow, PyTorch) required, making it suitable for edge devices and low-resource environments.

Key Features of the Contribution

1. Privacy-Preserving Mechanism
   Integrates a differential privacy module to add calibrated Gaussian noise to local gradients, ensuring compliance with privacy requirements while maintaining model utility.

2. High Training Efficiency
   Adopts asynchronous gradient aggregation on the server side—model updates can be triggered once a predefined ratio of clients (e.g., 80%) submit their gradients, significantly reducing training latency in large-scale federated systems.

3. Portability & Compatibility
   The code is written in standard C++17, with zero third-party dependencies. It can be compiled and deployed across x86, ARM, and embedded platforms without modification.

4. Production-Grade Robustness
   Includes gradient clipping to prevent gradient explosion, numerical stability optimizations (e.g., clamping input values in the sigmoid function to avoid overflow), and thread-safe random number generation for multi-client parallel training.

5. End-to-End Testability
   Comes with built-in utilities for generating simulated binary classification datasets, multi-threaded client training, and model evaluation (accuracy calculation on test sets), enabling out-of-the-box validation.

Code Overview

The contribution consists of the following core components:

• DifferentialPrivacy class: Manages Gaussian noise injection to local gradients for privacy protection.
• FLClient class: Simulates client-side local training, including forward propagation, gradient computation, and adaptive learning rate adjustment.
• FLServer class: Implements asynchronous gradient aggregation, global model updates, and model performance evaluation on test datasets.
• Auxiliary functions: Simulated dataset generation, multi-threaded client training orchestration, and result visualization helpers.

Use Cases

This implementation is applicable to a wide range of privacy-sensitive scenarios:

• Financial Risk Control: Joint training of credit scoring models across banks without sharing customer data.
• Smart Home IoT: Collaborative training of user behavior classification models across multiple smart devices, protecting user privacy.
• Medical Imaging Analysis: Federated training of lesion detection models across hospitals, complying with strict medical data privacy laws.
• Industrial Quality Inspection: Joint training of defect detection models across factories, enabling "data sharing without data leaving the factory".

Next Steps

I am ready to take the following actions to integrate this contribution into the project:

1. Submit a pull request (PR) with well-commented, consistently formatted code that aligns with the project's existing code style guidelines.
2. Add comprehensive unit tests and user documentation for the FL module, including compilation instructions, API references, and usage examples.
3. Adjust the implementation to fit the project's directory structure and build system (e.g., CMake, Makefile) if needed.
4. Address all review feedback promptly and iterate on the code to ensure it meets the project's quality standards.

Additional information

1.The code is compatible with C++17 and above.
2.No third-party dependencies (only standard libraries).
3.The implementation has been tested with simulated datasets (test accuracy ~85% for binary classification tasks)
4.The code follows modern C++ best practices (RAII, thread safety, exception safety).