12 citations · 22 across the 6 of their papers we have counts for
6 papers
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
Xin-Chun Li, Shaoming Song, Yinchuan Li +4
In some real-world applications, data samples are usually distributed on local devices, where federated learning (FL) techniques are proposed to coordinate decentralized clients wi…
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Xin-Chun Li, Wen-Shu Fan, Shaoming Song +4
Knowledge Distillation (KD) aims at transferring the knowledge of a well-performed neural network (the {\it teacher}) to a weaker one (the {\it student}). A peculiar phenomenon is…
Avoid Overfitting User Specific Information in Federated Keyword Spotting
Xin-Chun Li, Jin-Lin Tang, Shaoming Song +5
Keyword spotting (KWS) aims to discriminate a specific wake-up word from other signals precisely and efficiently for different users. Recent works utilize various deep networks to…
Federated Learning with Position-Aware Neurons
Xin-Chun Li, Yi-Chu Xu, Shaoming Song +4
Federated Learning (FL) fuses collaborative models from local nodes without centralizing users' data. The permutation invariance property of neural networks and the non-i.i.d. data…
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
Xin-Chun Li, Le Gan, De-Chuan Zhan +3
Although federated learning (FL) has recently been proposed for efficient distributed training and data privacy protection, it still encounters many obstacles. One of these is the…
Loosely Coupled Federated Learning Over Generative Models
Shaoming Song, Yunfeng Shao, Jian Li
Federated learning (FL) was proposed to achieve collaborative machine learning among various clients without uploading private data. However, due to model aggregation strategies, e…