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• W3153669236 abstract "Federated learning (FL) is an emerging distributed machine learning paradigm that protects privacy and tackles the problem of isolated data islands. At present, there are two main communication strategies of FL: synchronous FL and asynchronous FL. The advantages of synchronous FL are that the model has high precision and fast convergence speed. However, this synchronous communication strategy has the risk that the central server waits too long for the devices, namely, the straggler effect which has a negative impact on some time-critical applications. Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash. Therefore, we combine the advantages of these two strategies to propose a clustered semi-asynchronous federated learning (CSAFL) framework. We evaluate CSAFL based on four imbalanced federated datasets in a non-IID setting and compare CSAFL to the baseline methods. The experimental results show that CSAFL significantly improves test accuracy by more than +5% on the four datasets compared to TA-FedAvg. In particular, CSAFL improves absolute test accuracy by +34.4% on non-IID FEMNIST compared to TA-FedAvg." @default.
• W3153669236 created "2021-04-26" @default.
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• W3153669236 date "2021-04-16" @default.
• W3153669236 modified "2023-10-18" @default.
• W3153669236 title "CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework" @default.
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• W3153669236 doi "https://doi.org/10.48550/arxiv.2104.08184" @default.
• W3153669236 hasPublicationYear "2021" @default.
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