Edge AI applications enable edge devices to collaboratively learn a model via repeated model aggregations, aiming to utilize the distributed data on the devices for achieving high model accuracy. Existing methods either leverage a centralized server to directly aggregate the model updates from edge devices or need a central coordinator to group the edge devices for localized model aggregations. The centralized server (or coordinator) has a performance bottleneck and a high cost of collecting the global state needed for making the grouping decision in large-scale networks. In this paper, we propose an Autonomous Model Aggregation (AMA) method for large-scale decentralized learning on edge devices. Instead of needing a central coordinator to group the edge devices, AMA allows the edge devices to autonomously form groups using a highly efficient protocol, according to model functional similarity and historical grouping information. Moreover, AMA adopts a reinforcement learning approach to optimize the size of each group. Evaluation results on our self-developed edge computing testbed demonstrate that AMA outperforms the benchmark approaches by up to 20.71% in accuracy and reduced the convergence time by 75.58%.

Distributed learning on edges aims at training the AI model collaboratively in a network of edge devices via frequent model aggregations. Achieving the desired training performance requires the aggregation structure and frequency to fit well with the dynamic edge environment. Existing works often consider the optimization of either aggregation structure or frequency, assuming that the edge environment is stable and deterministic. In this paper, we propose a novel approach,AutoSF, to automatically optimize the aggregation structure and frequency jointly in dynamic edge computing so as to minimize the global loss function. The main idea of AutoSF is that when the edge environment changes, the automated machine learning approach is triggered to find out the near-optimal aggregation structure and frequency that adapt to time-varying edge resources. When the environment keeps unchanged, a heuristic approach is used to tune the aggregation structure and frequency to further tame the heterogeneity caused by data distributions. We validate the effectiveness of AutoSF via numerical experiments with real datasets on our self-developed edge computing testbed. Evaluation results demonstrate that AutoSF outperforms the benchmark approaches by up to 16.3× speedups in convergence speed and 31.0$\%$increases in training accuracy.