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LLM-QAT: Data-Free Quantization Aware Training for Large Language Models., , , , , , , , and . CoRR, (2023)TAN without a burn: Scaling Laws of DP-SGD., , and . CoRR, (2022)Green Federated Learning., , , , , , , , , and . CoRR, (2023)And the Bit Goes Down: Revisiting the Quantization of Neural Networks., , , , and . ICLR, OpenReview.net, (2020)Training with Quantization Noise for Extreme Model Compression., , , , , , and . CoRR, (2020)CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning., , and . ICLR, OpenReview.net, (2023)Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 11888-11904. PMLR, (2023)Reconciling Security and Communication Efficiency in Federated Learning., , , , , and . IEEE Data Eng. Bull., 46 (1): 67-78 (2023)LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference., , , , , , and . ICCV, page 12239-12249. IEEE, (2021)Efficiency and Redundancy in Deep Learning Models: Theoretical Considerations and Practical Applications. (Efficience et redondance dans les modèles d'apprentissage profond : considérations théoriques et applications pratiques).. École normale supérieure de Lyon, France, (2021)