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cs.LG2021★ 10 cited
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update
Junyu Luo, Jianlei Yang, Xucheng Ye +2
Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on het…
cs.LG2020
FedSiam: Towards Adaptive Federated Semi-Supervised Learning
Zewei Long, Liwei Che, Yaqing Wang +5
Federated learning (FL) has emerged as an effective technique to co-training machine learning models without actually sharing data and leaking privacy. However, most existing FL me…