Integrated Communication and Computation for Edge Intelligence
Over-the-air computation(AirComp)technology leverages the waveform superposition characteristics of wireless channels to further enhance the efficiency of wireless data aggregation,enabling model update information to be aggregated"in one shot".This achieves a conver-gence of communication networks and computational power networks,exemplifying the concept of"network and computation fusion".How-ever,channel fading and noise may introduce aggregation distortion during this process.Additionally,the quality of update information and the transmission energy consumption of edge devices can impact model aggregation and convergence efficiency.Therefore,we establish an Air-Comp enabled federated learning system and propose a dynamic device scheduling mechanism to address issues related to channel interfer-ence,efficient data transmission,and data distortion.Specifically,an appropriate number of higher-quality devices are selected to participate in model training while satisfying receiving signal-to-noise ratio conditions.It utilizes gradient importance,channel conditions,and transmission energy consumption to assess device quality and retains and accumulates gradients from unselected devices to accelerate convergence.The problem is modeled and solved based on the Lyapunov optimization theory.Simulation results demonstrate that this mechanism achieves higher training accuracy,faster convergence speed,and a certain level of robustness against varying noise power levels.
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