No abstract

To address the big data challenges, multi-party collaborative training, such as distributed learning and federated learning, has recently attracted attention. However, traditional multi-party collaborative training algorithms were mainly designed for balanced data mining tasks and are intended to optimize accuracy (e.g., cross-entropy). The data distribution in many real-world applications is skewed and classifiers, which are trained to improve accuracy, perform poorly when applied to imbalanced data tasks since models could be significantly biased toward the primary class. Therefore, the Area Under Precision-Recall Curve (AUPRC) was introduced as an effective metric. Although single-machine AUPRC maximization methods have been designed, multi-party collaborative algorithm has never been studied. The change from the single-machine to the multi-party setting poses critical challenges. For example, existing single-machine-based AUPRC maximization algorithms maintain an inner state for local each data point, thus these methods are not applicable to large-scale online multi-party collaborative training due to the dependence on each local data point.To address the above challenge, we study serverless multi-party collaborative AUPRC maximization problem since serverless multiparty collaborative training can cut down the communications cost by avoiding the server node bottleneck, and reformulate it as a conditional stochastic optimization problem in a serverless multi-party collaborative learning setting and propose a new ServerLess biAsed sTochastic gradiEnt (SLATE) algorithm to directly optimize the AUPRC. After that, we use the variance reduction technique and propose ServerLess biAsed sTochastic gradiEnt with Momentumbased variance reduction (SLATE-M) algorithm to improve the

Parallel optimization has become popular for large-scale learning in the past decades. However, existing methods suffer from huge computational costs, memory usage, and communication burden in high-dimensional scenarios. To address the challenges, we propose a new accelerated doubly sparse asynchronous learning (DSAL) method for stochastic composite optimization, under which two algorithms are proposed on shared-memory and distributed-memory architecture respectively, which only conducts gradient descent on the nonzero coordinates (data sparsity) and active set (model sparsity). The proposed algorithm can converge much faster and achieve significant speedup by simultaneously enjoying the sparsity of the model and data. Moreover, by sending the gradients on the active set only, communication costs are dramatically reduced. Theoretically, we prove that the proposed method achieves the linear convergence rate with lower overall complexity and can achieve the model identification in a finite number of iterations almost surely. Finally, extensive experimental results on benchmark datasets confirm the superiority of our proposed method.