Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigm
This work addresses delay reduction for efficient WFL integration in 6G networks, representing an incremental improvement over existing methods.
The paper tackles the problem of reducing delay in wireless federated learning (WFL) for 6G networks by introducing a Compute-then-Transmit NOMA (CT-NOMA) protocol, which allows concurrent local training and simultaneous parameter transmission, and simulation results show it effectively reduces delay compared to a time-division multiple access benchmark.
As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay for wireless federated learning (WFL), which is of paramount importance for the efficient integration of WFL in the sixth generation of wireless networks (6G). To this end, in this second part we introduce and optimize a novel communication protocol for WFL networks, that is based on non-orthogonal multiple access (NOMA). More specifically, the Compute-then-Transmit NOMA (CT-NOMA) protocol is introduced, where users terminate concurrently the local model training and then simultaneously transmit the trained parameters to the central server. Moreover, two different detection schemes for the mitigation of inter-user interference in NOMA are considered and evaluated, which correspond to fixed and variable decoding order during the successive interference cancellation process. Furthermore, the computation and communication resources are jointly optimized for both considered schemes, with the aim to minimize the total delay during a WFL communication round. Finally, the simulation results verify the effectiveness of CT-NOMA in terms of delay reduction, compared to the considered benchmark that is based on time-division multiple access.