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Wireless federated learning (WFL) undergoes a communication bottleneck inuplink, limiting the number of users that can upload their local models in eachglobal aggregation round. This paper presents a new multi-carriernon-orthogonal multiple-access (MC-NOMA)-empowered WFL system under an adaptivelearning setting of Flexible Aggregation. Since a WFL round accommodates bothlocal model training and uploading for each user, the use of FlexibleAggregation allows the users to train different numbers of iterations perround, adapting to their channel conditions and computing resources. The keyidea is to use MC-NOMA to concurrently upload the local models of the users,thereby extending the local model training times of the users and increasingparticipating users. A new metric, namely, Weighted Global Proportion ofTrained Mini-batches (WGPTM), is analytically established to measure theconvergence of the new system. Another important aspect is that we maximize theWGPTM to harness the convergence of the new system by jointly optimizing thetransmit powers and subchannel bandwidths. This nonconvex problem is convertedequivalently to a tractable convex problem and solved efficiently usingvariable substitution and Cauchy's inequality. As corroborated experimentallyusing a convolutional neural network and an 18-layer residential network, theproposed MC-NOMA WFL can efficiently reduce communication delay, increase localmodel training times, and accelerate the convergence by over 40%, compared toits existing alternative.

Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocation