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Intelligent, large-scale IoT ecosystems have become possible due to recentadvancements in sensing technologies, distributed learning, and low-powerinference in embedded devices. In traditional cloud-centric approaches, rawdata is transmitted to a central server for training and inference purposes. Onthe other hand, Federated Learning migrates both tasks closer to the edge nodesand endpoints. This allows for a significant reduction in data exchange whilepreserving the privacy of users. Trained models, though, may under-perform indynamic environments due to changes in the data distribution, affecting themodel's ability to infer accurately; this is referred to as concept drift. Suchdrift may also be adversarial in nature. Therefore, it is of paramountimportance to detect such behaviours promptly. In order to simultaneouslyreduce communication traffic and maintain the integrity of inference models, weintroduce FLARE, a novel lightweight dual-scheduler FL framework thatconditionally transfers training data, and deploys models between edge andsensor endpoints based on observing the model's training behaviour andinference statistics, respectively. We show that FLARE can significantly reducethe amount of data exchanged between edge and sensor nodes compared tofixed-interval scheduling methods (over 5x reduction), is easily scalable tolarger systems, and can successfully detect concept drift reactively with atleast a 16x reduction in latency.

FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments