Accelerating Decentralized Federated Learning With Probabilistic Communication in Heterogeneous Edge Computing

Decentralized federated learning (DFL) has gained popularity for training machine learning models on massive data in edge computing, as it avoids the potential bottleneck of conventional parameter server architectures. However, the existing DFL solutions typically use deterministic topologies that struggle with both system heterogeneity and non-IID local data, resulting in high bandwidth costs and slow convergence rates. In this paper, we propose a novel mechanism called Communication-efficient Decentralized Federated Learning (CedFL) to accelerate model training. In CedFL, each worker will communicate with each of its neighbors (i.e., model exchange) according to a certain probability at each epoch, so as to reduce bandwidth consumption. To this end, we then propose an efficient algorithm to adaptively determine the optimal probability for each worker pair according to real-time system situations (e.g., data distribution and bandwidth resource). Our proposed mechanism has been extensively tested on classical models and datasets, and the results demonstrate its high effectiveness. CedFL has been shown to reduce completion time for model training by approximately 55% and improve test accuracy by 11% under the bandwidth constraint, compared to state-of-the-art solutions.