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Found 2 papers, 2 papers with code
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach
By comparing their performance to (bitwidth-dedicated) QAT, existing bitwidth adaptive QAT and vanilla meta-learning, we find that merging bitwidths into meta-learning tasks achieves a higher level of robustness.
Personalized Federated Learning with Server-Side Information
Personalized Federated Learning (FL) is an emerging research field in FL that learns an easily adaptable global model in the presence of data heterogeneity among clients.
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