In this paper, we propose an opportunistic scheme for the transmission of model updates from Federated Learning (FL) clients to the server, where clients are wireless mobile users. This proposal aims to opportunistically take advantage of the proximity of users to the base station or the general condition of the wireless transmission channel, rather than traditional synchronous transmission. In this scheme, during the training, intermediate model parameters are uploaded to the server, opportunistically and based on the wireless channel condition. Then, the proactively-transmitted model updates are used for the global aggregation if the final local model updates are delayed. We apply this novel model transmission scheme to one of our previous work, which is a hybrid split and federated learning (HSFL) framework for UAVs. Simulation results confirm the superiority of using proactive transmission over the conventional asynchronous aggregation scheme for the staled model by obtaining higher accuracy and more stable training performance. Test accuracy increases by up to 13.47% with just one round of extra transmission.

翻译：本文提出了一种从联邦学习客户端到服务器模型更新的伺机传输方案，其中客户端为无线移动用户。该方案旨在伺机利用用户与基站的邻近性或无线传输信道的整体状况，而非传统的同步传输。在该方案中，训练过程中，中间模型参数根据无线信道状况伺机上传至服务器。然后，若最终本地模型更新延迟，则利用这些主动传输的模型更新进行全局聚合。我们将这一新颖的模型传输方案应用于我们先前提出的用于无人机的混合拆分与联邦学习框架。仿真结果证实，相比针对陈旧模型的传统异步聚合方案，采用主动传输可获得更高的准确率和更稳定的训练性能，仅需一轮额外传输，测试准确率即可提升高达13.47%。