A greedy shard-assignment protocol lets a joining node fetch model state from multiple neighbors in parallel, cutting scale-out delay to about one second and keeping other scaling events under 20 ms.
Federated fine-tuning of large language models under heterogeneous tasks and client resources,
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Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training
A greedy shard-assignment protocol lets a joining node fetch model state from multiple neighbors in parallel, cutting scale-out delay to about one second and keeping other scaling events under 20 ms.