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For highly distributed environments such as edge computing, collaborativelearning approaches eschew the dependence on a global, shared model, in favorof models tailored for each location. Creating tailored models for individuallearning contexts reduces the amount of data transfer, while collaborationamong peers provides acceptable model performance. Collaboration assumes,however, the availability of knowledge transfer mechanisms, which are nottrivial for deep learning models where knowledge isn't easily attributed toprecise model slices. We present Canoe - a framework that facilitates knowledgetransfer for neural networks. Canoe provides new system support for dynamicallyextracting significant parameters from a helper node's neural network and usesthis with a multi-model boosting-based approach to improve the predictiveperformance of the target node. The evaluation of Canoe with different PyTorchand TensorFlow neural network models demonstrates that the knowledge transfermechanism improves the model's adaptiveness to changes up to 3.5X compared tolearning in isolation, while affording several magnitudes reduction in datamovement costs compared to federated learning.

Canoe : A System for Collaborative Learning for Neural Nets