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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.
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server
Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i. e., limited computational resources and low training efficiency.
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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