Security challenges in distributed training

Technical note. This is a threat-model checklist for research and evaluation.

Identify trusted components

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Distributed jobs depend on workers, launch services, storage, communication libraries, images, and checkpoints. Document who can change code, add a worker, read state, issue a restart, or retrieve a credential. PyTorch’s distributed documentation provides the implementation context for worker groups and communication.

Compose protections around the workload

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• Limit credentials and administrative access to the scope each service needs.
• Verify the provenance and integrity of code, configuration, and checkpoint artifacts.
• Define network boundaries and peer authentication for the deployment.
• Isolate jobs and state what is trusted inside each execution boundary.
• Test failure handling, checkpoint recovery, and the evidence retained afterward.

Confidential execution and memory-protection mechanisms may address parts of this threat model. They require appropriate hardware, software integration, and stated assumptions. Neither prevents all data poisoning, establishes training correctness, or replaces an operational policy.

Evaluate protections and training efficiency together, recording the workload, configuration, baseline, and remaining risks. This is a research direction; it does not announce an AGIACC training appliance.