Intelligent, large-scale IoT ecosystems have become possible due to recent advancements in sensing technologies, distributed learning, and low-power inference in embedded devices. In traditional cloud-centric approaches, raw data is transmitted to a central server for training and inference purposes. On the other hand, Federated Learning migrates both tasks closer to the edge nodes and endpoints. This allows for a significant reduction in data exchange while preserving the privacy of users. Trained models, though, may under-perform in dynamic environments due to changes in the data distribution, affecting the model's ability to infer accurately; this is referred to as concept drift. Such drift may also be adversarial in nature. Therefore, it is of paramount importance to detect such behaviours promptly. In order to simultaneously reduce communication traffic and maintain the integrity of inference models, we introduce FLARE, a novel lightweight dual-scheduler FL framework that conditionally transfers training data, and deploys models between edge and sensor endpoints based on observing the model's training behaviour and inference statistics, respectively. We show that FLARE can significantly reduce the amount of data exchanged between edge and sensor nodes compared to fixed-interval scheduling methods (over 5x reduction), is easily scalable to larger systems, and can successfully detect concept drift reactively with at least a 16x reduction in latency.

翻译：智能大规模物联网生态系统得益于传感技术、分布式学习以及嵌入式设备低功耗推理领域的最新进展已成为可能。在传统以云为中心的方法中，原始数据传输至中央服务器用于训练和推理。而联邦学习则将这两项任务迁移至更接近边缘节点和终端设备的位置，从而在显著减少数据交换的同时保护用户隐私。然而，由于数据分布发生变化，训练后的模型可能在动态环境中性能下降，影响模型准确推理的能力，这被称为概念漂移。此类漂移也可能具有对抗性。因此，及时检测此类行为至关重要。为在降低通信流量与维持推理模型完整性之间取得平衡，我们提出FLARE这一新型轻量级双调度联邦学习框架，其基于观察模型训练行为与推理统计信息，有条件地传输训练数据，并在边缘节点与传感器终端之间部署模型。研究表明，与固定间隔调度方法相比，FLARE可显著减少边缘节点与传感器节点之间的数据交换量（超过5倍缩减），易于扩展至更大规模系统，并能以至少16倍的延迟降低率成功实现概念漂移的被动检测。