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Intrusion detection through classifying incoming packets is a crucial functionality at the network edge, requiring accuracy, efficiency and scalability at the same time, introducing a great challenge. On the one hand, traditional table-based switch functions have limited capacity to identify complicated network attack behaviors. On the other hand, machine learning based methods providing high accuracy are widely used for packet classification, but they typically require packets to be forwarded to an extra host and therefore increase the network latency. To overcome these limitations, in this paper we propose an architecture with programmable data plane switches. We show that Binarized Neural Networks (BNNs) can be implemented as switch functions at the network edge classifying incoming packets at the line speed of the switches. To train BNNs in a scalable manner, we adopt a federated learning approach that keeps the communication overheads of training small even for scenarios involving many edge network domains. We next develop a prototype using the P4 language and perform evaluations. The results demonstrate that a multi-fold improvement in latency and communication overheads can be achieved compared to state-of the-art learning architectures. 

Legumes are the second most important family of crop plants. One defining feature of legumes is their unique ability to establish a nitrogen-fixing root nodule symbiosis with soil bacteria known as rhizobia. Since domestication from their wild relatives, crop legumes have been under intensive breeding to improve yield and other agronomic traits but with little attention paid to the belowground symbiosis traits. Theoretical models predict that domestication and breeding processes, coupled with high‐input agricultural practices, might have reduced the capacity of crop legumes to achieve their full potential of nitrogen fixation symbiosis. Testing this prediction requires characterizing symbiosis traits in wild and breeding populations under both natural and cultivated environments using genetic, genomic, and ecological approaches. However, very few experimental studies have been dedicated to this area of research. Here, we review how legumes regulate their interactions with soil rhizobia and how domestication, breeding and agricultural practices might have affected nodulation capacity, nitrogen fixation efficiency, and the composition and function of rhizobial community. We also provide a perspective on how to improve legume-rhizobial symbiosis in sustainable agricultural systems.