Sparse zeroth-order federated fine-tuning with shared seeds and GradIP-based early stopping matches or beats full-parameter ZO while using far less communication.
Fine-grained theoretical analysis of federated zeroth-order optimization.Advances in Neural Information Processing Systems, 36, 2024
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Mitigating Non-IID Drift in Zeroth-Order Federated LLM Fine-Tuning with Transferable Sparsity
Sparse zeroth-order federated fine-tuning with shared seeds and GradIP-based early stopping matches or beats full-parameter ZO while using far less communication.