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A First Look into the Carbon Footprint of Federated Learning., , , , , , , , and . J. Mach. Learn. Res., (2023)A first look into the carbon footprint of federated learning., , , , , , , and . CoRR, (2021)Federated Self-supervised Speech Representations: Are We There Yet?, , , , and . INTERSPEECH, page 3809-3813. ISCA, (2022)Federated Learning for Inference at Anytime and Anywhere., , , , , , , , and . CoRR, (2022)An Empirical study of Binary Neural Networks' Optimisation., , , and . ICLR (Poster), OpenReview.net, (2019)Degree-Quant: Quantization-Aware Training for Graph Neural Networks., , and . ICLR, OpenReview.net, (2021)Quantification of the 3D collagen network geometry in confocal reflection microscopy., , , , , and . ICIP, page 1791-1794. IEEE, (2015)Searching for Winograd-aware Quantized Networks., , , and . MLSys, mlsys.org, (2020)ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity., , , , , and . ICLR, OpenReview.net, (2022)Protea: Client Profiling within Federated Systems using Flower., , , , and . CoRR, (2022)