MimiC: Combating Client Dropouts in Federated Learning by Mimicking
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MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates

IEEE Transactions on Mobile Computing (IEEE TMC), 2023

21 June 2023

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Papers citing "MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates"

7 / 7 papers shown

Title
A Unified Analysis of Federated Learning with Arbitrary Client ParticipationNeural Information Processing Systems (NeurIPS), 2022 Maroun Touma Mingyue Ji FedML 414 76 0 31 Dec 2024
Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks R. Saha Mohamed Seif M. Yemini Andrea J. Goldsmith H. Vincent Poor FedML 175 5 0 06 Jun 2024
Dual-Delayed Asynchronous SGD for Arbitrarily Heterogeneous Data Xiaolu Wang Yuchang Sun Hoi-To Wai Jun Zhang 196 1 0 27 May 2024
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients Xiaolu Wang Zijian Li Shi Jin Jun Zhang FedML 280 6 0 17 Feb 2024
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated LearningIEEE Transactions on Mobile Computing (IEEE TMC), 2024 Yuchang Sun Marios Kountouris Jun Zhang FedML 204 3 0 24 Jan 2024
Green Edge AI: A Contemporary SurveyProceedings of the IEEE (Proc. IEEE), 2023 Yuyi Mao X. Yu Kaibin Huang Ying-Jun Angela Zhang Jun Zhang 310 49 0 01 Dec 2023
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous DataIEEE Transactions on Mobile Computing (IEEE TMC), 2023 Tailin Zhou Zehong Lin Jinchao Zhang Danny H. K. Tsang MoMe FedML 321 16 0 13 May 2023