Decentralized Over-the-Air Federated Learning in Full-Duplex MIMO Networks

Decentralized federated learning (FL) is capable of enabling efficient and robust collaborative model training with device-to-device (D2D) communications. However, most existing studies on decentralized FL employ half-duplex communication to achieve time-division model aggregation, which is inefficient in scenarios with massive geographically dispersed devices. To address this issue, we in this paper propose decentralized over-the-air FL (DOAFL) with full-duplex (FD) communication, where over-the-air computation (AirComp) and FD communication are fused together to enable parallel model exchange and aggregation, and antenna arrays are leveraged to suppress residual self-interference (SI). Specifically, we first conduct the convergence analysis for DOAFL to characterize the influence of the consensus error introduced by residual SI, channel fading, and receiver noise on the learning performance. Subsequently, we formulate a joint communication and computation (JC2) optimization problem with an objective to increase both the accuracy and time efficiency of the model training, followed by developing a JC2 design algorithm to efficiently optimize transceiver beamforming and computing frequencies. Simulation results verify the superiority of our proposed DOAFL in terms of training latency, residual SI suppression, and learning performance under low energy budgets.