Towards asynchronous federated learning for heterogeneous edge-powered internet of things

The advancement of the Internet of Things (IoT) brings new opportunities for collecting real-time data and deploying machine learning models. Nonetheless, an individual IoT device may not have adequate computing resources to train and deploy an entire learning model. At the same time, transmitting continuous real-time data to a central server with high computing resource incurs enormous communication costs and raises issues in data security and privacy. Federated learning, a distributed machine learning framework, is a promising solution to train machine learning models with resource-limited devices and edge servers. Yet, the majority of existing works assume an impractically synchronous parameter update manner with homogeneous IoT nodes under stable communication connections. In this paper, we develop an asynchronous federated learning scheme to improve training efficiency for heterogeneous IoT devices under unstable communication network. Particularly, we formulate an asynchronous federated learning model and develop a lightweight node selection algorithm to carry out learning tasks effectively. The proposed algorithm iteratively selects heterogeneous IoT nodes to participate in the global learning aggregation while considering their local computing resource and communication condition. Extensive experimental results demonstrate that our proposed asynchronous federated learning scheme outperforms the state-of-the-art schemes in various settings on independent and identically distributed (i.i.d.) and non-i.i.d. data distribution.