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.

翻译：联邦学习是机器学习领域的最新发展，它允许设备系统在不将数据共享到单个位置或设备的情况下训练一个或多个任务。然而，该框架仍需要集中式全局模型将各个模型整合为一个，且设备进行同步训练，这两点都可能成为联邦学习应用的潜在瓶颈。本文提出一种新颖的异步去中心化联邦终身学习（ADFLL）方法，该方法继承联邦学习的优点，无需中心节点或同步训练即可同时训练多个任务，从而克服传统联邦学习的潜在缺陷。我们在脑肿瘤分割（BRATS）数据集上展示了优异性能，用于在多图像序列和图像方向上定位左心室。我们的框架使智能体达到最佳性能，平均距离误差为7.81，优于传统全知智能体的11.81，并且经过八轮训练后，其性能显著（p=0.01）优于传统终身学习智能体（距离误差15.17）。此外，所有ADFLL智能体的性能均与传统终身学习智能体相当或更优。总之，我们开发的ADFLL框架相比传统强化学习智能体展现了卓越的性能和加速效果。