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Federated Learning for Inference at Anytime and Anywhere., , , , , , , , and . CoRR, (2022)Smart at what cost?: characterising mobile deep neural networks in the wild., , , , , and . Internet Measurement Conference, page 658-672. ACM, (2021)MELTing point: Mobile Evaluation of Language Transformers., , , and . CoRR, (2024)FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout., , , , , and . NeurIPS, page 12876-12889. (2021)DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device., , , , and . CoRR, (2021)SPINN: synergistic progressive inference of neural networks over device and cloud., , , , and . MobiCom, page 37:1-37:15. ACM, (2020)HAPI: Hardware-Aware Progressive Inference., , , and . ICCAD, page 91:1-91:9. IEEE, (2020)Adaptable mobile vision systems through multi-exit neural networks., , , and . MobiSys, page 575-576. ACM, (2022)ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning., , , , and . INTERSPEECH, page 2235-2239. ISCA, (2019)DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device., , , , and . ACM Trans. Embed. Comput. Syst., 21 (6): 71:1-71:24 (November 2022)