FedACT schedules devices across concurrent FL jobs via alignment scoring and fairness to reduce average job completion time by up to 8.3x and raise accuracy by up to 44.5% versus baselines.
Federated learning: Challenges, methods, and future directions
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Federated aggregation strategies show distinct performance trade-offs in accuracy, loss, and efficiency depending on whether client data distributions are homogeneous or heterogeneous.
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FedACT: Concurrent Federated Intelligence across Heterogeneous Data Sources
FedACT schedules devices across concurrent FL jobs via alignment scoring and fairness to reduce average job completion time by up to 8.3x and raise accuracy by up to 44.5% versus baselines.
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A Comparative Study of Federated Learning Aggregation Strategies under Homogeneous and Heterogeneous Data Distributions
Federated aggregation strategies show distinct performance trade-offs in accuracy, loss, and efficiency depending on whether client data distributions are homogeneous or heterogeneous.