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Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression
[article]
2021
arXiv
pre-print
Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the realistic environment in which federated learning methods must be able to operate reliably. Besides varying amounts of non-IID data at participants, the asynchronous setting models heterogeneous client participation due to available computational power and battery constraints and also accounts for delayed
arXiv:2111.13931v1
fatcat:gmh3c4pcsba7llfwfpufeey25e