71 citations · 72 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 71 cited
Federated Mutual Learning
Tao Shen, Jie Zhang, Xinkang Jia +6
Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing about…
cs.LG2020
Evaluation Framework For Large-scale Federated Learning
Lifeng Liu, Fengda Zhang, Jun Xiao +1
Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while kee…
cs.AI2020★ 1 cited
Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach
Zeyue Xue, Shuang Luo, Chao Wu +3
Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying o…