FLARE: Detection and Mitigation of Concept Drift for Federated Learning
  based IoT Deployments
Theo Chow Usman Raza Ioannis Mavromatis Aftab Khan
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.