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

no code implementations • 15 May 2023 • Theo Chow, Usman Raza, Ioannis Mavromatis, Aftab Khan

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.

Federated Learning Scheduling