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A Survey on Federated Learning for Resource-Constrained IoT Devices.

, , , , and . IEEE Internet Things J., 9 (1): 1-24 (2022)

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Skipping RNN State Updates without Retraining the Original Model., , , and . SenSys-ML, page 31-36. ACM, (2019)Understanding the Impact of Dynamic Channel Pruning on Conditionally Parameterized Convolutions., , , and . AIChallengeIoT@SenSys, page 27-33. ACM, (2020)Efficiently Adapting Pretrained Language Models To New Languages., , , and . CoRR, (2023)Ternary MobileNets via Per-Layer Hybrid Filter Banks., , , and . CoRR, (2019)Run-Time Efficient RNN Compression for Inference on Edge Devices., , , , and . EMC2@HPCA/CVPR/ISCA, page 26-30. IEEE, (2019)A Survey on Federated Learning for Resource-Constrained IoT Devices., , , , and . IEEE Internet Things J., 9 (1): 1-24 (2022)Measuring scheduling efficiency of RNNs for NLP applications., , , and . CoRR, (2019)PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts., , , , , , , , , and 16 other author(s). CoRR, (2022)PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts., , , , , , , , , and 17 other author(s). ACL (demo), page 93-104. Association for Computational Linguistics, (2022)Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products., , , , , , and . ACM J. Emerg. Technol. Comput. Syst., 17 (4): 46:1-46:18 (2021)