A Bayesian federated learning framework uses over-the-air superposition to aggregate local posterior distributions, with convergence analysis and power control, improving accuracy and calibration under scarce non-i.i.d. wireless data.
Model-contrastive federated learning,
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Distribution-Level AirComp for Wireless Federated Learning under Data Scarcity and Heterogeneity
A Bayesian federated learning framework uses over-the-air superposition to aggregate local posterior distributions, with convergence analysis and power control, improving accuracy and calibration under scarce non-i.i.d. wireless data.