CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework

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.

arXiv commentThis paper will be presented at IJCNN 2021

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Cited by in corpus (5)

• FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
• Asynchronous Federated Learning on Heterogeneous Devices: A Survey
• Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks
• Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks
• FedLesScan: Mitigating Stragglers in Serverless Federated Learning