flow-nexus-neural

To train neural networks in distributed sandboxes, you connect the Flow Nexus MCP server, authenticate your account, and configure your model for execution across multiple E2B nodes. This process deploys worker and aggregator nodes to run scalable training.

Federated learning is a distributed training approach that initializes clusters of worker and aggregator nodes across separate sandboxes. It allows models to train locally on decentralized data before aggregating updates, maintaining live monitoring throughout the process.

Yes, you can run transformer and LSTM model training on E2B sandboxes. The system supports single-node training and template deployment for feedforward, LSTM, GAN, autoencoder, and transformer architectures with tiered resource options.

Yes, distributed training requires authentication-aware cluster control to initialize clusters and deploy worker nodes. You must authenticate your account through the Flow Nexus MCP server before managing scalable execution across multiple nodes.

The best way to benchmark model latency after training is to use the lifecycle management features to validate performance. This allows you to measure benchmarking metrics and publish the validated model as a reusable template.

Deploying templates for federated learning requires model configuration and scalable execution across multiple nodes. Limitations include the need for authentication-aware cluster control and tiered resource management to validate performance across distributed sandboxes.