Projected Latent Distillation for Data-Agnostic Consolidation in distributed continual learning

In continual learning applications on-the-edge multiple self-centered devices (SCD) learn different local tasks independently, with each SCD only optimizing its own task. Can we achieve (almost) zero-cost collaboration between different devices? We formalize this problem as a Distributed Continual Learning (DCL) scenario, where SCDs greedily adapt to their own local tasks and a separate continual learning (CL) model perform a sparse and asynchronous consolidation step that combines the SCD models sequentially into a single multi-task model without using the original data. Unfortunately, current CL methods are not directly applicable to this scenario. We propose Data-Agnostic Consolidation (DAC), a novel double knowledge distillation method which performs distillation in the latent space via a novel Projected Latent Distillation loss. Experimental results show that DAC enables forward transfer between SCDs and reaches state-of-the-art accuracy on Split CIFAR100, CORe50 and Split TinyImageNet, both in single device and distributed CL scenarios. Somewhat surprisingly, a single out-of-distribution image is sufficient as the only source of data for DAC.