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Federated Learning (FL) enables multiple devices to collaboratively train a shared model while preserving data privacy. Ever-increasing model complexity coupled with limited memory resources on the participating devices severely bottlenecks the deplo
Externí odkaz:
http://arxiv.org/abs/2410.11577
Publikováno v:
2024 IEEE/ACM International Symposium on Quality of Service (IWQoS)
Federated learning (FL) coordinates multiple devices to collaboratively train a shared model while preserving data privacy. However, large memory footprint and high energy consumption during the training process excludes the low-end devices from cont
Externí odkaz:
http://arxiv.org/abs/2409.07202
Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices to collaboratively train a shared model while preserving data privacy. However, intensive memory footprint during the training process severely bottlenecks the d
Externí odkaz:
http://arxiv.org/abs/2408.10826
This paper presents ProFL, a novel progressive FL framework to effectively break the memory wall. Specifically, ProFL divides the model into different blocks based on its original architecture. Instead of updating the full model in each training roun
Externí odkaz:
http://arxiv.org/abs/2404.13349
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