The end-to-end Adaptive Local Learning (TALL) framework integrates a loss-driven Mixture-of-Experts module and adaptive weight adjustments to enhance customization and synchronization across diverse users.
By employing a loss-driven Mixture-of-Experts approach, TALL customizes learning models for individual users, overcoming discrepancies that often degrade performance in traditional methods.
The adaptive weight module is pivotal for synchronizing learning paces, ensuring that even when users have different learning speeds, the overall training objectives remain aligned.
Our experiments validate that TALL outperforms existing debiasing techniques, demonstrating significant improvements in both accuracy and fairness across varied datasets and user scenarios.
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