Dual-Segment Clustering Strategy for Federated Learning in Heterogeneous Environments

Federated learning (FL) is a distributed machine learning paradigm with high efficiency and low communication load, only transmitting parameters or gradients of network. However, the non-independent and identically distributed (Non-IID) data characteristic has a negative impact on this paradigm. Furthermore, the heterogeneity of communication quality will significantly affect the accuracy of parameter transmission, causing a degradation in the performance of the FL system or even preventing its convergence. This letter proposes a dual-segment clustering (DSC) strategy, which first clusters the clients according to the heterogeneous communication conditions and then performs a second clustering by the sample size and label distribution, so as to solve the problem of data and communication heterogeneity. Experimental results show that the DSC strategy proposed in this letter can improve the convergence rate of FL, and has superiority on accuracy in a heterogeneous environment compared with the classical algorithm of cluster.

Low-latency auditory spatial attention detection based on spectro-spatial features from EEG

Detecting auditory attention based on brain signals enables many everyday applications, and serves as part of the solution to the cocktail party effect in speech processing. Several studies leverage the correlation between brain signals and auditory stimuli to detect the auditory attention of listeners. Recently, studies show that the alpha band (8-13 Hz) EEG signals enable the localization of auditory stimuli. We believe that it is possible to detect auditory spatial attention without the need of auditory stimuli as references. In this work, we use alpha power signals for automatic auditory spatial attention detection. To the best of our knowledge, this is the first attempt to detect spatial attention based on alpha power neural signals. We propose a spectro-spatial feature extraction technique to detect the auditory spatial attention (left/right) based on the topographic specificity of alpha power. Experiments show that the proposed neural approach achieves 81.7% and 94.6% accuracy for 1-second and 10-second decision windows, respectively. Our comparative results show that this neural approach outperforms other competitive models by a large margin in all test cases.