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This paper proposes using communication pipelining to enhance the wirelessspectrum utilization efficiency and convergence speed of federated learning inmobile edge computing applications. Due to limited wireless sub-channels, asubset of the total clients is scheduled in each iteration of federatedlearning algorithms. On the other hand, the scheduled clients wait for theslowest client to finish its computation. We propose to first cluster theclients based on the time they need per iteration to compute the localgradients of the federated learning model. Then, we schedule a mixture ofclients from all clusters to send their local updates in a pipelined manner. Inthis way, instead of just waiting for the slower clients to finish theircomputation, more clients can participate in each iteration. While the timeduration of a single iteration does not change, the proposed method cansignificantly reduce the number of required iterations to achieve a targetaccuracy. We provide a generic formulation for optimal client clustering underdifferent settings, and we analytically derive an efficient algorithm forobtaining the optimal solution. We also provide numerical results todemonstrate the gains of the proposed method for different datasets and deeplearning architectures.

Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning