  Federated learning is a recent development in the machine learning area that
allows a system of devices to train on one or more tasks without sharing their
data to a single location or device. However, this framework still requires a
centralized global model to consolidate individual models into one, and the
devices train synchronously, which both can be potential bottlenecks for using
federated learning. In this paper, we propose a novel method of asynchronous
decentralized federated lifelong learning (ADFLL) method that inherits the
merits of federated learning and can train on multiple tasks simultaneously
without the need for a central node or synchronous training. Thus, overcoming
the potential drawbacks of conventional federated learning. We demonstrate
excellent performance on the brain tumor segmentation (BRATS) dataset for
localizing the left ventricle on multiple image sequences and image
orientation. Our framework allows agents to achieve the best performance with a
mean distance error of 7.81, better than the conventional all-knowing agent's
mean distance error of 11.78, and significantly (p=0.01) better than a
conventional lifelong learning agent with a distance error of 15.17 after eight
rounds of training. In addition, all ADFLL agents have comparable or better
performance than a conventional LL agent. In conclusion, we developed an ADFLL
framework with excellent performance and speed-up compared to conventional RL
agents.
