Decentralized Online Learning: Take Benefits from Others' Data without
Sharing Your Own to Track Global Trend

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Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend

29 January 2019

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Papers citing "Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend"

4 / 4 papers shown

Title
Distributed Online Non-convex Optimization with Composite Regret Zhanhong Jiang Aditya Balu Xian Yeow Lee Young M. Lee Chinmay Hegde Soumik Sarkar 97 4 0 21 Sep 2022
Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication Sen Lin M. Dedeoglu Junshan Zhang FedML 48 6 0 15 Dec 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 287 6,329 0 10 Dec 2019
DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization Parvin Nazari Davoud Ataee Tarzanagh George Michailidis ODL 96 68 0 25 Jan 2019

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