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Federated Learning over Wireless IoT Networks with Optimized Communication and Resources
[article]
2021
arXiv
pre-print
To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of
arXiv:2110.11775v1
fatcat:22ii7wz3nva4fb53got3nglf3e