Pegasus: Efficient Asynchronous Three-Layer Federated Learning

Compared to conventional two-layer federated learning (FL), three-layer FL, which adds a layer of edge servers between the central server and clients, is less studied but could provide better training performance in terms of reducing elapsed wall-clock time to complete training. However, three-layer FL inherits and magnifies some inherent challenges in two-layer FL, such as client heterogeneity. With different computing capabilities and network connections, slow clients require exceedingly long training times. To alleviate the negative effect due to slow clients, this paper is the first to study asynchronous three-layer FL, where edge servers conduct local aggregation without waiting for slow clients and the central server aggregates updates from fast edge servers. Unfortunately, asynchronous three-layer FL could suffer from performance degradation as when aggregating updates from slow clients and edge servers, those updates are not computed based on the newest global model. Therefore, we propose a staleness-aware framework, Pegasus, with newly designed client selection, compression, and update aggregation mechanisms to improve every important aspect during training. Our extensive evaluation of different training tasks demonstrates that Pegasus can achieve a reduction in elapsed wall-clock training time by at least 46.8% with an increase in converged accuracy of the trained global model by up to 0.3% as compared to the state-of-the-art.