01572nas a2200205   4500000000100000008004100001653003000042653002300072653000800095653000900103100002700112700001500139700001800154245005700172856007200229300001100301490000700312520103300319022001401352       2023                            d10aEmbedded machine learning10afederated learning10aIoT10aLoRa1 aNil Llisterri-Giménez1 aJoan Solé1 aFelix Freitag00aEmbedded federated learning over a LoRa mesh network  uhttps://www.sciencedirect.com/science/article/pii/S1574119223000779  a1018190 v933 aIn on-device training of machine learning models on microcontrollers a neural network is trained on the device. A specific approach for collaborative on-device training is federated learning. In this paper, we propose embedded federated learning on microcontroller boards using the communication capacity of a LoRa mesh network. We apply a dual board design: The machine learning application that contains a neural network is trained for a keyword spotting task on the Arduino Portenta H7. For the networking of the federated learning process, the Portenta is connected to a TTGO LORA32 board that operates as a router within a LoRa mesh network. We experiment the federated learning application on the LoRa mesh network and analyze the network, system, and application level performance. The results from our experimentation suggest the feasibility of the proposed system and exemplify an implementation of a distributed application with re-trainable compute nodes, interconnected over LoRa, entirely deployed at the tiny edge.  a1574-1192