Ray-Based Federated & Decentralized ML Framework
Comprehensive tools for distributed machine learning research with advanced privacy guarantees, flexible network topologies, and Byzantine-robust aggregation.
What is Murmura?
Murmura is a comprehensive Ray-based framework for federated and decentralized machine learning. Built for researchers and developers, it provides tools for distributed ML with advanced privacy guarantees and flexible network topologies.
Decentralized
Built for peer-to-peer environments with no central authority
Privacy-Preserving
Keeps data private while enabling collaborative learning
Experimental
Designed for research and development of new ML techniques
Key Features
Murmura provides a comprehensive toolkit for decentralized federated learning research and development.
Ray-Based Distributed Computing
Multi-node cluster support with automatic actor lifecycle management and intelligent resource optimization.
Flexible Network Topologies
Star, ring, complete graph, line, and custom topologies with automatic compatibility validation.
Comprehensive Differential Privacy
Client-level DP with Opacus integration, RDP privacy accounting, and automatic noise calibration.
Byzantine-Robust Aggregation
Trimmed mean and secure aggregation strategies for adversarial environments.
Real-Time Monitoring
Privacy budget tracking, resource usage monitoring, and comprehensive metrics export.
Multiple Aggregation Strategies
FedAvg, TrimmedMean, and GossipAvg with privacy-enabled variants for diverse use cases.
Beta Release 1.0.1
Murmura has reached beta status with core features implemented and tested. The framework is now available for researchers to experiment with federated and decentralized learning scenarios, complete with privacy guarantees and flexible deployment options.
- Ray-based distributed computing frameworkCompleted
- FedAvg, TrimmedMean, and GossipAvg algorithmsCompleted
- Differential privacy with Opacus integrationCompleted
- Byzantine-robust aggregation strategiesCompleted
- Flexible network topologies supportCompleted
- Real-time metrics and monitoringCompleted
- Advanced network fault simulationIn Progress
- Homomorphic encryption integrationPlanned
Future Roadmap
Our vision for Murmura extends beyond current capabilities. Here's what we're planning for the future.
Enhanced Privacy Techniques
Implement homomorphic encryption and secure multi-party computation for stronger privacy guarantees in federated learning.
Advanced Network Simulation
Realistic network conditions simulation with fault injection, latency modeling, and dynamic topology changes.
AI Agent Integration
Autonomous learning agents for dynamic environments with self-adaptive learning strategies and intelligent resource allocation.
Production Deployment Tools
Enterprise-ready deployment capabilities with monitoring, scaling, and management tools for real-world federated learning.
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