V-LETO combines evolving class prototypes with Fisher-information-based parameter freezing to handle class and feature incremental learning in vertical federated learning, reporting large gains over baselines.
PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
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abstract
We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on data features vertically partitioned across multiple owners while keeping raw data on an owner's device. To link entities shared across different datasets' partitions, we use Private Set Intersection on IDs associated with data points. To demonstrate the validity of the proposed framework, we present the training of a simple dual-headed split neural network for a MNIST classification task, with data samples vertically distributed across two data owners and a data scientist.
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Vertical Federated Continual Learning via Evolving Prototype Knowledge
V-LETO combines evolving class prototypes with Fisher-information-based parameter freezing to handle class and feature incremental learning in vertical federated learning, reporting large gains over baselines.