We consider federated learning (FL) with multiple wireless edge servers having their own local coverage. We focus on speeding up training in this increasingly practical setup. Our key idea is to utilize the clients located in the overlapping coverage areas among adjacent edge servers (ESs); in the model-downloading stage, the clients in the overlapping areas receive multiple models from different ESs, take the average of the received models, and then update the averaged model with their local data. These clients send their updated model to multiple ESs by broadcasting, which acts as bridges for sharing the trained models between servers. Even when some ESs are given biased datasets within their coverage regions, their training processes can be assisted by adjacent servers through the clients in their overlapping regions. As a result, the proposed scheme does not require costly communications with the central cloud server (located at the higher tier of edge servers) for model synchronization, significantly reducing the overall training time compared to the conventional cloud-based FL systems. Extensive experimental results show remarkable performance gains of our scheme compared to existing methods. Our design targets latency-sensitive applications where edge-based FL is essential, e.g., when a number of connected cars/drones must cooperate (via FL) to quickly adapt to dynamically changing environments.

中文翻译：

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FedMes：利用多个边缘服务器加速联合学习


我们考虑使用具有自己的本地覆盖范围的多个无线边缘服务器进行联邦学习（FL）。我们专注于在这个日益实用的设置中加快培训速度。我们的核心思想是利用位于相邻边缘服务器（ES）之间重叠覆盖区域的客户端；在模型下载阶段，重叠区域的客户端从不同ES接收多​​个模型，对接收到的模型取平均值，然后用本地数据更新平均模型。这些客户端通过广播将更新后的模型发送到多个 ES，充当服务器之间共享训练模型的桥梁。即使某些 ES 在其覆盖区域内获得有偏差的数据集，它们的训练过程也可以通过重叠区域中的客户端得到相邻服务器的协助。因此，所提出的方案不需要与中央云服务器（位于较高层的边缘服务器）进行昂贵的通信来进行模型同步，与传统的基于云的 FL 系统相比，显着减少了总体训练时间。大量的实验结果表明，与现有方法相比，我们的方案具有显着的性能提升。我们的设计针对延迟敏感的应用，其中基于边缘的 FL 至关重要，例如，当许多联网汽车/无人机必须合作（通过 FL）以快速适应动态变化的环境时。