WebA Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning. CoRR abs/2201.02932 (2024) [i10] view. ... SphereFed: Hyperspherical Federated Learning. CoRR abs/2207.09413 (2024) 2024 [c21] view. electronic edition via DOI; unpaywalled version; references & citations; authority control: export record. WebSphereFed: Hyperspherical Federated Learning. Xin Dong, Sai Qian Zhang, Ang Li, H. T. Kung. ... A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning. Sai Qian Zhang, Jieyu Lin, Qi Zhang.

Chapter cover SphereFed: Hyperspherical Federated Learning

WebSphereFed: Hyperspherical Federated Learning Xin Dong, Sai Qian Zhang, Ang Li, H.T. Kung ; Abstract "Federated Learning aims at training a global model from multiple decentralized … WebFederated Learning aims at training a global model from multiple decentralized devices (i.e. clients) without exchanging their private local data. A key challenge is the handling of non … geddy lee at blue jays game

dblp: Sai Qian Zhang

Web20. júl 2024 · 【1】 SphereFed: Hyperspherical Federated Learning ... 【2】 Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design ... Web36.Machine Learning(机器学习) Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning; 35.Feature Learning(联邦学习) SphereFed: Hyperspherical Federated Learning; Image Coding for Machines with Omnipotent Feature Learning; Addressing Heterogeneity in Federated Learning via … Web13. okt 2024 · Federated learning decentralizes deep learning by removing the need to pool data into a single location. Instead, the model is trained in multiple iterations at different sites. For example, say three hospitals decide to team up and build a model to help automatically analyze brain tumor images. If they chose to work with a client-server ... dbs rolling check