Joint Resource Allocation to Minimize Execution Time of Federated Learning in Cell-Free Massive MIMO

Understand

Due to its communication efficiency and privacy-preserving capability, federated learning (FL) has emerged as a promising framework for machine learning in 5G-and-beyond wireless networks.

• Of great interest is the design and optimization of new wireless network structures that support the stable and fast operation of FL.
• Cell-free massive multiple-input multiple-output (CFmMIMO) turns out to be a suitable candidate, which allows each communication round in the iterative FL process to be stably executed within a large-scale coherence time.
• Aiming to reduce the total execution time of the FL process in CFmMIMO, this paper proposes choosing only a subset of available users to participate in FL.