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Recent advances in machine learning, wireless communication, and mobilehardware technologies promisingly enable federated learning (FL) over massivemobile edge devices, which opens new horizons for numerous intelligent mobileapplications. Despite the potential benefits, FL imposes huge communication andcomputation burdens on participating devices due to periodical globalsynchronization and continuous local training, raising great challenges tobattery constrained mobile devices. In this work, we target at improving theenergy efficiency of FL over mobile edge networks to accommodate heterogeneousparticipating devices without sacrificing the learning performance. To thisend, we develop a convergence-guaranteed FL algorithm enabling flexiblecommunication compression. Guided by the derived convergence bound, we design acompression control scheme to balance the energy consumption of local computing(i.e., "working") and wireless communication (i.e., "talking") from thelong-term learning perspective. In particular, the compression parameters areelaborately chosen for FL participants adapting to their computing andcommunication environments. Extensive simulations are conducted using variousdatasets to validate our theoretical analysis, and the results also demonstratethe efficacy of the proposed scheme in energy saving.

To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices