non-i.i.d. FEMNIST dataset (Figure 9.6) and non-convex loss functions for Sent140 (Figure 9.7). Under all scenarios, we observe test accuracy fluctuations, especially for the FEMNIST dataset as it is a highly non-i.i.d. scenario and almost every device has a distinct data distribution. Nevertheless, one can observe similar performance between the proposed 9.6 Numerical Results Original FedAvg NOMA, Adaptive quantization NOMA, Adaptive sparsification Communication time (in seconds) Figure 9.5 Test accuracy comparison between original TDMA-based FedAvg and NOMA compression-based FedAvg update with communication time. Figure 9.6 Test accuracy on FEMNIST datasets: Test accuracy comparison vs communication rounds. Original FedAvg NOMA, Adaptive quantization NOMA, Adaptive sparsification Round algorithm and the original FedAvg in terms of accuracy. Notice that the original FedAvg and our adaptive sparsification scheme have almost identical result at each round; hence, they can be hardly differentiated from the curves. Again the adaptive quantization gives a relatively worse performance than other two schemes. From latency performance perspective, in the final round of FEMNIST dataset, to obtain 79.5% of accuracy, TDMA-based original FedAvg takes approximately 1600 s, while both NOMA-enabled adaptive compression schemes consume around 200 s. Similarly, Sent140 achieves 73.5% accuracy with 75 s with our scheme but over 510s with TDMA-based FedAvg. These results have again proved the remarkable low latency performance of the proposed algorithm. 9 Wireless Systems for Distributed Machine Learning Figure 9.7 Test accuracy on Sent140 datasets: Test accuracy comparison vs communication rounds. Original FedAvg NOMA, Adaptive quantization NOMA, Adaptive sparsification Round 9.7 Summary In this work, we proposed to apply NOMA in the uplink FL model update. We considered wireless fading channels during the update process and adaptively compressed gradient values according to either sparsification or quantization method. To furthe