A production-grade distributed federated learning framework that enables privacy-preserving image classification across multiple clients without sharing raw data.
Built with privacy, scalability, and production readiness in mind
Raw data never leaves client devices. Implements differential privacy with configurable ε and δ parameters for mathematical privacy guarantees.
Support for 50+ concurrent clients with horizontal scaling. Auto-scaling AWS infrastructure with load balancing and fault tolerance.
Optimized gRPC communication protocols, model compression, and 25% latency reduction compared to centralized approaches.
Containerized deployment with Docker, comprehensive monitoring, structured logging, and automated error recovery mechanisms.
Built on PyTorch with FedAvg algorithm, convergence detection, and support for CNN architectures on standard datasets.
Complete Terraform configuration for AWS deployment with auto-scaling groups, load balancers, and managed databases.
Distributed, scalable, and privacy-preserving by design
Validated performance metrics and scalability results
Multiple deployment strategies for different environments
Built with modern, production-grade technologies