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Towards Taming the Resource and Data Heterogeneity in Federated Learning., , , , , , , and . OpML, page 19-21. USENIX Association, (2019)QP-LDP for better global model performance in federated learning., , , , , and . MSN, page 422-426. IEEE, (2022)FedAT: a high-performance and communication-efficient federated learning system with asynchronous tiers., , , , , and . SC, page 60. ACM, (2021)User-Aware Multi-Interest Learning for Candidate Matching in Recommenders., , , , , , , and . SIGIR, page 1326-1335. ACM, (2022)Federated Multi-task Hierarchical Attention Model for Sensor Analytics., , , and . CoRR, (2019)TiFL: A Tier-based Federated Learning System., , , , , , , , , and . HPDC, page 125-136. ACM, (2020)IMRL: An Improved Inertial-Aided KLT Feature Tracker., , and . CIS/RAM, page 392-397. IEEE, (2019)Variational Progressive-Transfer Network for Soft Sensing of Multirate Industrial Processes., , and . IEEE Trans. Cybern., 52 (12): 12882-12892 (2022)Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM framework., , , , , and . CoRR, (2021)A Deep Probabilistic Transfer Learning Framework for Soft Sensor Modeling With Missing Data., , , and . IEEE Trans. Neural Networks Learn. Syst., 33 (12): 7598-7609 (2022)