The continuous emergence of new attack classes challenges intrusion detection in edge Internet of Things (IoT) networks. Although federated learning enables distributed devices to collaboratively train a shared detector without exchanging raw traffic data, most federated intrusion detection systems assume a fixed label space. Retraining with all historical data incurs substantial storage and computation costs, whereas updating only with newly collected samples can cause catastrophic forgetting. The detector must mitigate catastrophic forgetting of previously observed attack classes while preserving sufficient new-class plasticity to learn emerging attacks under highly non-IID device data, intermittent client availability, constrained local memory, and repeated communication over bandwidth-limited and intermittently connected links. To address these challenges, this paper proposes EdgeFedCIL, a communication-efficient federated class-incremental intrusion detection framework. EdgeFedCIL preserves historical knowledge through client-local replay and knowledge distillation while reducing repeated model transmission through adaptive low-rank compression, quantization, and error feedback. A classifier-head protection strategy further limits compression-induced degradation of class discrimination. Experiments on public intrusion-detection datasets show that EdgeFedCIL achieves competitive or superior detection and historical-knowledge retention performance, particularly under highly heterogeneous client distributions, while reducing cumulative client-to-server model transmission by up to approximately 10.54 times relative to full-precision transmission. These results demonstrate the effectiveness of EdgeFedCIL for continual and communication-efficient intrusion detection in resource-constrained edge IoT networks.
Genetic improvement is essential for improved rice production in the high-elevation, cool-climate regions of Ethiopia. We conducted QTL-seq analysis using 846 RIL F5 populations of ‘X-Jigna’, a dominant rice cultivar in Ethiopia, × ‘Hitomebore’, a cold-tolerant elite Japanese cultivar, in field trials in Japan. We detected three QTLs for cold tolerance for inducing spikelet sterility under cold at reproductive stage (qSFC5, qSFC8, and qSFC10) and one for seed shattering habit (qSHT1). In haplotype analysis using indel markers in an F6 population in Japan and Ethiopia, qSFC5 and qSFC8 were confirmed in Japan and qSHT1 was confirmed in both Japan and Ethiopia. Markers for the cold tolerance and seed shattering will be useful to speed up breeding in Ethiopia.
BACKGROUND: Plant organ shape is an important trait in terms of economic efficiency in agriculture. The club-like or spindle-shaped underground tubers of Chinese yam (Dioscorea polystachya Turcz.) impede mechanical harvest resulting in unprofitable cultivation. RESULTS: We performed genome-wide DNA methylation sequencing of two closely related Chinese yam tuber shape variants F60 (long, thin) and F2000 (short, thick) to investigate potential epigenetic effects on tuber development and shaping. Analysis of differentially methylated regions (DMRs) led to the identification of 1,513 hyper- or hypomethylated regions in 763 DMR-associated genes of which the majority were detected in the CHG methylation context. Gene Ontology (GO) term analysis revealed enriched DMR-associated genes related to anatomical structure development, hormone response, and photomorphogenesis. DMRs in brassinosteroid (BR) pathway-related genes, in particular BR signaling genes, were detected. We further identified hyperDMRs and hypoDMRs in putative circadian rhythm-related genes such as DpVOZ1, DpPIE1 and DpSPA1 as well as several putative organ shape-related genes (DpIQD13, DpTCPs). CONCLUSION: A potential network consisting of components of the BR signaling pathway, and circadian clock as well as microtubule-associated proteins was suggested to coordinate tuberization and affect tuber shape of Chinese yam.
