In this letter, we study federated learning in a cellular system with a base station (BS) and a large number of users with local data sets. We show that multichannel random access can provide a better performance than sequential polling when some users are unable to compute local updates (due to other tasks) or in dormant state. In addition, for better aggregation in federated learning, the access probabilities of users can be optimized for given local updates. To this end, we formulate an optimization problem and show that a distributed approach can be used within federated learning to adaptively decide the access probabilities.

中文翻译：

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多通道 ALOHA 联合学习

在这封信中，我们研究了蜂窝系统中的联邦学习，该系统具有基站 (BS) 和具有本地数据集的大量用户。我们表明，当某些用户无法计算本地更新（由于其他任务）或处于休眠状态时，多通道随机访问可以提供比顺序轮询更好的性能。此外，为了在联邦学习中更好地聚合，可以针对给定的本地更新优化用户的访问概率。为此，我们制定了一个优化问题，并表明可以在联邦学习中使用分布式方法来自适应地决定访问概率。