In the domain of computer architecture, Federated Learning (FL) is a paradigm of distributed machine learning in edge systems. However, the systems’ Non-Independent and Identically Distributed (Non-IID) data negatively affect the convergence efficiency of the global model, since only a subset of these data samples is beneficial for accelerating model convergence. In pursuit of this subset, a reliable approach involves determining a measure of validity to rank the samples within the dataset. In this paper, we propose the BHerd strategy, which selects a beneficial herd of local gradients to accelerate the convergence of the FL model. Specifically, we map the distribution of the local dataset to the local gradients and use the Herding strategy to obtain a permutation of the set of gradients, where the more advanced gradients in the permutation are closer to the average of the set of gradients. These top portions of the gradients will be selected and sent to the server for global aggregation. We conduct experiments on different datasets, models, and scenarios by building a prototype system, and experimental results demonstrate that our BHerd strategy is effective in selecting beneficial local gradients to mitigate the effects brought by the Non-IID dataset.

The heavy ball momentum technique is widely used in accelerating the machine learning training process, which has demonstrated significant practical success in optimization tasks. However, most heavy ball methods require a preset hyperparameter that will result in excessive tuning, and a calibrated fixed hyperparameter may not lead to optimal performance. In this paper, we propose an adaptive criterion for the choice of the normalized momentum-related hyperparameter, motivated by the quadratic optimization training problem, to eliminate the adverse for tuning the hyperparameter and thus allow for a computationally efficient optimizer. We theoretically prove that our proposed adaptive method promises convergence for L-Lipschitz functions. In addition, we verify its practical efficiency on existing extensive machine learning benchmarks for image classification tasks. The numerical results show that besides the speed improvement, our proposed methods enjoy advantages, including more robust to large learning rates and better generalization.