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Resource-Constrained Federated Learning with Heterogeneous Labels and Models
[article]
2020
arXiv
pre-print
Various IoT applications demand resource-constrained machine learning mechanisms for different applications such as pervasive healthcare, activity monitoring, speech recognition, real-time computer vision, etc. This necessitates us to leverage information from multiple devices with few communication overheads. Federated Learning proves to be an extremely viable option for distributed and collaborative machine learning. Particularly, on-device federated learning is an active area of research,
arXiv:2011.03206v1
fatcat:yk4hxhyd7vahfb7qvfi44id2ji