This paper studies the design of wireless federated learning (FL) for simultaneously training multiple machine learning models. We consider round robin device-model assignment and downlink beamforming for concurrent multiple model updates. After formulating the joint downlink-uplink transmission process, we derive the per-model global update expression over communication rounds, capturing the effect of beamforming and noisy reception. To maximize the multi-model training convergence rate, we derive an upper bound on the optimality gap of the global model update and use it to formulate a multi-group multicast beamforming problem. We show that this problem can be converted to minimizing the sum of inverse received signal-to-interference-plus-noise ratios, which can be solved efficiently by projected gradient descent. Simulation shows that our proposed multi-model FL solution outperforms other alternatives, including conventional single-model sequential training and multi-model zero-forcing beamforming.

翻译：本文研究同时训练多个机器学习模型的无线联邦学习设计。我们考虑循环轮询的设备-模型分配方案以及用于并发多模型更新的下行波束赋形。在构建联合上下行传输过程后，推导了设备每轮全局更新的表达式，该表达式捕捉了波束赋形与噪声接收的影响。为最大化多模型训练的收敛速率，我们给出了全局模型更新最优性差距的上界，并据此构建了多组多播波束赋形问题。研究表明该问题可转化为最小化接收信干噪比倒数之和的形式，可通过投影梯度下降高效求解。仿真结果表明，本文提出的多模型联邦学习方案优于传统单模型序列训练及多模型迫零波束赋形等替代方法。