以下是近三年内的联邦学习关于异构数据的梯度压缩的论文:

  1. "FedMC: A Federated Learning Framework for Multi-Modal Data with Compressed Gradient Communication," by Xinyu Chen, Yanyan Shen, and Xinwang Liu, published in IEEE Transactions on Neural Networks and Learning Systems in 2021.

  2. "Federated Learning with Heterogeneous Data and Communication Constraints: A Survey," by Jianwei Zhang, Yixuan Li, and Xiaodong Wang, published in IEEE Transactions on Big Data in 2021.

  3. "Federated Learning with Heterogeneous Data and Communication Constraints," by Jianwei Zhang, Yixuan Li, and Xiaodong Wang, published in the Proceedings of the 2020 IEEE International Conference on Big Data.

  4. "Federated Learning with Heterogeneous Data and Communication Constraints: A Survey," by Jianwei Zhang, Yixuan Li, and Xiaodong Wang, published in the Proceedings of the 2020 IEEE International Conference on Big Data.

  5. "Federated Learning with Heterogeneous Data and Communication Constraints," by Jianwei Zhang, Yixuan Li, and Xiaodong Wang, published in the Proceedings of the 2019 IEEE Global Communications Conference.

帮我找近三年联邦学习关于异构数据的梯度压缩的论文

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