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FedLore: Communication and Memory Efficient Federated Learning via Shared Gradient Low-Rank Projection
Federated training of foundation models is constrained by client memory and communication costs. LoRA-based methods reduce these costs through low-rank adapters, but their fixed rank budget can limit adaptation. Gradient low-rank optimization offers greater flexibility, yet independently chosen clie
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