oblems with MNIST (Mod- ified National Institute of Standards and Technology database, a database of handwritten digit images) and Federated Extended MNIST (FEMNIST) datasets and non-convex loss function on text sentiment analysis task on tweets from Sentiment140 (Sent140). The con- vex problem is solved with LeNet-300-100 model, which is a fully connected network with two hidden layers. The first layer consists of 300 neurons and the second layer consists of 100 neurons. The non-convex problem uses a long short-term memory (LSTM) classi- fier. We further employ the datasets from [103] SO that the data points on different devices are non-i.i.d., i.e. the number of data points and their high-level model representations vary across different devices. Each device trains the learning model individually and then uploads the model parameters to the PS for the weighted average. The statistics of the datasets are summarized in Table 9.2. For hyperparameters in FL, we use batch size B = 10 on all datasets. The learning rate and number of communication rounds are fixed for each dataset but may vary among different datasets. Specifically, we use learning rate n = 0.001 and maximum communication round T = 100 for MNIST, n = 0.003, T = 300 for FEMNIST, and n = 0.05, T = 100 for Sent140. For each user, it uses the majority portion of the data for training and the rest for testing. Testing is performed at each device and accuracy is calculated by taking the average across all the devices. Figure 9.4 presents the test accuracy result from the MNIST dataset. In the proposed scheme, NOMA is used in the uplink channel and the model parameters are compressed with either adaptive quantization or sparsification. The average compression ratio for adaptive quantization at each round is 0.55 for K = 10 and 0.33 for K = 20. For adaptive Table 9.2 Statistics of datasets. Dataset No. of arameters (P) No. of devices (N) No. of data (D) MNIST 266,610 1,000 69,035 FEMNIST 266,610 18,345 Sent140 243,861 40,783 9 Wireless Systems for Distributed