Privacy-Preserving Federated Learning System for Distributed Machine Learning

The objective of this project is to develop a federated learning system that enables multiple clients to collaboratively train a machine learning model without sharing raw data. The system ensures data privacy by keeping datasets local while aggregating only model updates securely.

Study centralized vs federated learning architectures and associated privacy risks.

Understand gradient sharing, model aggregation, and data leakage threats.

Design a distributed system with a central aggregation server.

Implement local model training at client nodes using sample datasets.

Develop secure aggregation mechanisms for model updates.

Apply encryption to protect transmitted model parameters.

Prevent reconstruction attacks using privacy-preserving techniques.

Simulate multiple client environments for training.

Evaluate model accuracy compared to centralized learning.

Measure communication overhead and scalability.

Document security, efficiency, and real-world application scenarios