<ul><li>As data volumes expand rapidly, distributed machine learning has become essential for addressing the growing computational demands of modern AI systems. Low-Rank Adaptation (LoRA) offers a promising solution to this problem by personalizing low-rank updates rather than optimizing the entire model.</li><li>To address the limitation of manual configuration of the initial rank in LoRA-enabled distributed learning, the researchers propose AutoRank, an adaptive rank-setting algorithm inspired by the bias-variance trade-off. AutoRank leverages the MCDA method TOPSIS to dynamically assign local ranks based on the complexity of each participant's data.</li><li>Experimental results demonstrate that AutoRank significantly reduces computational overhead, enhances model performance, and accelerates convergence in highly heterogeneous federated learning environments. Through its strong adaptability, AutoRank offers a scalable and flexible solution for distributed machine learning.</li></ul>

AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning

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