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Resource-Efficient Federated Learning
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Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device capabilities, and participant availability as deployments scale, which can impact both model convergence and bias. Existing FL schemes use random participant selection to improve fairness; however, this can result in inefficient use of resources and lower quality training. In this work, we systematically address the question of resource efficiency in FL, showing the benefits of intelligent participant selection, and incorporation of updates from straggling participants. We demonstrate how these factors enable resource efficiency while also improving trained model quality.
Forward citations
Cited by 2 Pith papers
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RIFLES: Resource-effIcient Federated LEarning via Scheduling
RIFLES schedules federated learning clients by forecasting device availability with a CNN-LSTM model, claiming faster convergence and lower dropout than Random, FedCS, and REFL in simulation.
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FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments
FlexFed combines offline local training with performance-based retention of rare-class samples and reports reduced catastrophic forgetting in federated human activity recognition.
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