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Federated Learning aims at training a global model from multiple decentralized devices (i.e.
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Chen, C., Xu, H., Wang, W., Li, B., Li, B., Chen, L., Zhang, G.: Communication-efficient federated learning with adaptive parameter freezing. In: 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS). pp. 1–11. IEEE (2021)
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Li, D., Wang, J.: Fedmd: Heterogenous federated learning via model distillation. NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality (2019)
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Raghu, A., Raghu, M., Bengio, S., Vinyals, O.: Rapid learning or feature reuse? towards understanding the effectiveness of maml. International Conference on Learning Representations (2019)
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Shoham, N., Avidor, T., Keren, A., Israel, N., Benditkis, D., Mor-Yosef, L., Zeitak, I.: Overcoming forgetting in federated learning on non-iid data. NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality (2019)
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Diao, E., Ding, J., Tarokh, V.: Heterofl: Computation and communication efficient federated learning for heterogeneous clients. International Conference on Learning Representations (2021)
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Duan, J.H., Li, W., Lu, S.: Feddna: Federated learning with decoupled normalization-layer aggregation for non-iid data. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 722–737. Springer (2021)
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Li, Q., Diao, Y., Chen, Q., He, B.: Federated learning on non-iid data silos: An experimental study. IEEE International Conference on Data Engineering (2021)
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Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10713–10722 (2021)
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Luo, M., Chen, F., Hu, D., Zhang, Y., Liang, J., Feng, J.: No fear of heterogeneity: Classifier calibration for federated learning with non-iid data. 35th Conference on Neural Information Processing Systems (2021)
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Muthukumar, V., Narang, A., Subramanian, V., Belkin, M., Hsu, D., Sahai, A.: Classification vs regression in overparameterized regimes: Does the loss function matter? Journal of Machine Learning Research 22
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Oh, J., Kim, S., Yun, S.Y.: Fedbabu: Towards enhanced representation for federated image classification. International Conference on Learning Representations (2021)
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Puigcerver, J., Riquelme, C., Mustafa, B., Renggli, C., Pinto, A.S., Gelly, S., Keysers, D., Houlsby, N.: Scalable transfer learning with expert models. International Conference on Learning Representations (2021)
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Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečnỳ, J., Kumar, S., McMahan, H.B.: Adaptive federated optimization. International Conference on Learning Representations (2021)
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Shao, S., Xing, L., Wang, Y., Xu, R., Zhao, C., Wang, Y., Liu, B.: Mhfc: Multi-head feature collaboration for few-shot learning. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 4193–4201 (2021)
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Singhal, K., Sidahmed, H., Garrett, Z., Wu, S., Rush, J., Prakash, S.: Federated reconstruction: Partially local federated learning. Advances in Neural Information Processing Systems 34
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Sun, B., Huo, H., Yang, Y., Bai, B.: Partialfed: Cross-domain personalized federated learning via partial initialization. Advances in Neural Information Processing Systems 34
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Xu, C., Hong, Z., Huang, M., Jiang, T.: Acceleration of federated learning with alleviated forgetting in local training. In: International Conference on Learning Representations (2021)
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Yoon, J., Jeong, W., Lee, G., Yang, E., Hwang, S.J.: Federated continual learning with weighted inter-client transfer. In: International Conference on Machine Learning. pp. 12073–12086. PMLR (2021)
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Yoon, T., Shin, S., Hwang, S.J., Yang, E.: Fedmix: Approximation of mixup under mean augmented federated learning. International Conference on Learning Representations (2021)
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Zhao, N., Wu, Z., Lau, R.W., Lin, S.: What makes instance discrimination good for transfer learning? International Conference on Learning Representations (2021)
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Zhu, Z., Hong, J., Zhou, J.: Data-free knowledge distillation for heterogeneous federated learning. In: International Conference on Machine Learning. pp. 12878–12889. PMLR (2021)
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Xu, A., Huang, H.: Coordinating momenta for cross-silo federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 8735–8743 (2022)
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