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Efficient Asynchronous Federated Learning with Sparsification and Quantization
As the bandwidth between the devices and the server is relatively low, the communication of intermediate data becomes a bottleneck.
Multi-Job Intelligent Scheduling with Cross-Device Federated Learning
The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process.
Efficient Device Scheduling with Multi-Job Federated Learning
However, the scheduling of devices for multiple jobs with FL remains a critical and open problem.
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