A Hivemind/Petals dev here. As far as I understand, most federated learning methods can't efficiently train very large models (with billions of parameters) because they repeat some calculations on many peers and/or involve excess communication.
In contrast, the training methods implemented in Hivemind struggle to minimize compute and communication but don't provide data privacy guarantees. This is mostly okay for LLMs, since they are trained on public data scraped from the Internet anyway.
In contrast, the training methods implemented in Hivemind struggle to minimize compute and communication but don't provide data privacy guarantees. This is mostly okay for LLMs, since they are trained on public data scraped from the Internet anyway.