Murmura is a comprehensive framework for federated and decentralized machine learning. Built for researchers and developers, it provides tools for distributed machine learning simulation with advanced privacy guarantees and flexible network topologies. The framework supports both centralized federated learning and fully decentralized peer-to-peer learning environments, with features including multiple network topologies, Byzantine-robust aggregation strategies, comprehensive differential privacy support, and intelligent resource management.If you use this repository in your work, please cite the following: @INPROCEEDINGS{rangwala2026murmura, author={Rangwala, Murtaza and Sinnott, Richard O and Buyya, Rajkumar}, booktitle={2026 IEEE 26th International Symposium on Cluster, Cloud and Internet Computing (CCGrid)}, title={Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT}, year={2026}, publisher = {IEEE Press}, address = {New Jersey, USA}, pages = {510-519}, doi={10.1109/CCGrid68966.2026.00061}}

@INPROCEEDINGS{rangwala2026murmura, author={Rangwala, Murtaza and Sinnott, Richard O and Buyya, Rajkumar}, booktitle={2026 IEEE 26th International Symposium on Cluster, Cloud and Internet Computing (CCGrid)}, title={Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT}, year={2026}, publisher = {IEEE Press}, address = {New Jersey, USA}, pages = {510-519}, doi={10.1109/CCGrid68966.2026.00061}}