Scenario 2: Cross-Device Federated Machine Learning Using Minimal Resources скачать в хорошем качестве

Scenario 2: Cross-Device Federated Machine Learning Using Minimal Resources

Federated machine learning for cross-device use cases is a crucial but challenging area of research. It requires a strong understanding of machine learning processes, distributed computing, and the constraints of edge devices. Setting up an experimental environment that accurately reflects real-world scenarios can be difficult. In this video, the Scaleout team guides you through the process of setting up a cross-device federated learning experiment. By using just one physical or virtual machine to run the clients, with FEDn Studio managing the server-side components, you can create an effective experimental setup. FEDn Studio is a public SaaS platform available for free for research and development projects. The command used to initiate the clients with constrained resources: systemd-run --scope -p MemoryMax=500M -p MemorySwapMax=100M -p CPUQuota=10% --unit=client02 fedn run client -in /home/salman/fedn/examples/mnist-pytorch/client2/client.yaml --secure=True --force-ssl && sudo journalctl -u client02 -f Docker containers can also be used. Important Links: Scenario 1 video: The Importance of Distributed Setups in Federated Learning: Insights from Scaleout's FEDn Framework (   • Scenario 1: The Importance of Distributed ...   ) FEDn Studio: https://fedn.scaleoutsystems.com/ Quick start guide: https://fedn.readthedocs.io/en/latest... #federatedlearning #artificialintelligence #privacy #machinelearning #artificialintelligence #distributedcomputing #mlmodels #security #iot #network #modeltraining #edgecomputing