Optimal beamforming in over-the-air federated learning for efficient model aggregation

초록

Federated learning (FL) enables distributed model training while preserving privacy, but frequent updates from many devices create substantial communication challenges. Over-the-air computation (AirComp) offers a solution by aggregating updates directly over wireless channels through signal superposition, reducing overhead. However, AirComp can increase the mean squared error (MSE) of aggregated signals, affecting model accuracy. This paper introduces a beamforming optimization framework for AirComp in FL systems, jointly optimizing base station beamforming and device transmission scaling to minimize MSE. Two algorithms are developed: a high-performance convex method (Miso-CVX) and a lower-complexity subgradient method (Miso-Subgradient), both balancing signal misalignment and noise. Extensive simulations show improved aggregation accuracy, convergence speed, and robustness to channel variations. © 2025 The Authors

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