FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems

15 April 2021

Papers citing "FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems"

10 / 10 papers shown

Title
ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning Zhangchen Xu Fengqing Jiang Luyao Niu Jinyuan Jia Bo Li Radha Poovendran FedML 57 2 0 31 May 2024
Flashback: Understanding and Mitigating Forgetting in Federated Learning Mohammed Aljahdali A. Abdelmoniem Marco Canini Samuel Horváth 37 3 0 08 Feb 2024
Heterogeneous Federated Learning: State-of-the-art and Research Challenges Mang Ye Xiuwen Fang Bo Du PongChi Yuen Dacheng Tao FedML AAML 44 250 0 20 Jul 2023
A Survey of Federated Evaluation in Federated Learning Behnaz Soltani Yipeng Zhou Venus Haghighi John C. S. Lui FedML 46 12 0 14 May 2023
DPP-based Client Selection for Federated Learning with Non-IID Data Yuxuan Zhang Chao Xu Howard H. Yang Xijun Wang Tony Q.S. Quek FedML 51 5 0 30 Mar 2023
FedAgg: Adaptive Federated Learning with Aggregated Gradients Wenhao Yuan Xuehe Wang FedML 48 0 0 28 Mar 2023
Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout Jian Xu Mei Yang Wenbo Ding Shao-Lun Huang FedML 30 3 0 11 Mar 2023
Resource-Efficient Federated Learning A. Abdelmoniem Atal Narayan Sahu Marco Canini Suhaib A. Fahmy FedML 34 55 0 01 Nov 2021
Towards Personalized Federated Learning A. Tan Han Yu Li-zhen Cui Qiang Yang FedML AI4CE 209 847 0 01 Mar 2021
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure Moming Duan Duo Liu Xinyuan Ji Renping Liu Liang Liang Xianzhang Chen Yujuan Tan FedML 19 61 0 14 Oct 2020