 Machine Learning K = 20, Original FedAvg K = 20, NOMA, Adaptive quantization K = 20, NOMA, Adaptive sparsification K = 10, Original FedAvg K = 10, NOMA, Adaptive quantization K = 10, NOMA, Adaptive sparsification Round Figure 9.4 Test accuracy comparison under different scenarios, when K = 10, K = 20, and original TDMA-based FedAvg with our proposed NOMA compression-based FedAvg. sparsification, the average compression ratio is 0.53 and 0.31. As a comparison, original FedAvg algorithm [128] with no gradient compression (32-bit per parameter) is also implemented with TDMA as the access scheme. It can be readily observed that all the schemes except the adaptive quantization with K = 20 users achieve a similar accuracy (over 80%) at round 100. The reason is that, when K = 20 users participate the model updates simultaneously with NOMA, mutual interference within each round causes significant data rate degradation for the first few decoded users, which then leads to a more aggressive compression strategy, especially for quantization where most of the parameters are set to 0. While Figure 9.4 shows that the proposed scheme has a comparable accuracy with the original FedAvg. For simplicity, we only show the scenario with K = 10. With the simulation settings, each round for NOMA and compression-based FedAvg scheme corresponds to tk + T second and each round for the original FedAvg scheme corresponds to Ktd + Td seconds. Thus NOMA and compression-based FedAvg scheme takes around 70s time to achieve 85% accuracy. In Figure 9.5, in order to achieve the same accuracy, the original TDMA-based FedAvg takes more than 500 s. NOMA-aided FL can save 7.4x communication time dur- ing the update process. Alternatively, with 500 s training in NOMA-based protocol, we see the accuracy improves only from 85% to 88.6% for adaptive quantization and to 90% for adaptive sparsification. Notice that under K = 20, the time difference is more prominent. The proposed NOMA-enabled adaptive compression scheme is also proved to be effec- tive for 