The typical federated learning workflow requires communication between a central server and a large set of clients synchronizing model parameters between each other. The current frameworks use communication protocols not suitable for resource-constrained devices and are either hard to deploy or require high-throughput links not available on these devices. In this paper, we present a generic message framework using CBOR for communication with existing federated learning frameworks optimised for use with resource-constrained devices and low power and lossy network links. We evaluate the resulting message sizes against JSON serialized messages where compare both with model parameters resulting in optimal and worst case serialization length, and with a real-world LeNet-5 model. Our benchmarks show that with our approach, messages are up to 75 % smaller in size when compared to the JSON alternative.

翻译：典型的联邦学习工作流需要中央服务器与大量客户端之间进行通信，以同步彼此之间的模型参数。现有框架使用不适用于资源受限设备的通信协议，这些协议要么难以部署，要么需要这些设备无法提供的高吞吐量链路。本文提出了一种通用消息框架，采用CBOR格式与现有联邦学习框架进行通信，并针对资源受限设备及低功耗有损网络链路进行了优化。我们评估了所生成的消息大小，并与JSON序列化消息进行了对比，比较了两种方法在模型参数最优和最差情况下的序列化长度，以及实际LeNet-5模型的实际表现。基准测试结果显示，与JSON方案相比，采用本方法的消息大小可缩减高达75%。