185 citations · 463 across the 31 of their papers we have counts for
9 papers · 1 filter
More is Different: Prototyping and Analyzing a New Form of Edge Server with Massive Mobile SoCs
Li Zhang, Zhe Fu, Boqing Shi +7
Huge energy consumption poses a significant challenge for edge clouds. In response to this, we introduce a new type of edge server, namely SoC Cluster, that orchestrates multiple l…
Federated Few-Shot Learning for Mobile NLP
Dongqi Cai, Shangguang Wang, Yaozong Wu +2
Natural language processing (NLP) sees rich mobile applications. To support various language understanding tasks, a foundation NLP model is often fine-tuned in a federated, privacy…
Towards Practical Few-shot Federated NLP
Dongqi Cai, Yaozong Wu, Haitao Yuan +3
Transformer-based pre-trained models have emerged as the predominant solution for natural language processing (NLP). Fine-tuning such pre-trained models for downstream tasks often…
Device-centric Federated Analytics At Ease
Li Zhang, Junji Qiu, Shangguang Wang +1
Nowadays, high-volume and privacy-sensitive data are generated by mobile devices, which are better to be preserved on devices and queried on demand. However, data analysts still la…
Mandheling: Mixed-Precision On-Device DNN Training with DSP Offloading
Daliang Xu, Mengwei Xu, Qipeng Wang +6
This paper proposes Mandheling, the first system that enables highly resource-efficient on-device training by orchestrating the mixed-precision training with on-chip Digital Signal…
Accelerating Vertical Federated Learning
Dongqi Cai, Tao Fan, Yan Kang +4
Privacy, security and data governance constraints rule out a brute force process in the integration of cross-silo data, which inherits the development of the Internet of Things. Fe…
Boosting DNN Cold Inference on Edge Devices
Rongjie Yi, Ting Cao, Ao Zhou +3
DNNs are ubiquitous on edge devices nowadays. With its increasing importance and use cases, it's not likely to pack all DNNs into device memory and expect that each inference has b…
FedAdapter: Efficient Federated Learning for Modern NLP
Dongqi Cai, Yaozong Wu, Shangguang Wang +2
Transformer-based pre-trained models have revolutionized NLP for superior performance and generality. Fine-tuning pre-trained models for downstream tasks often requires private dat…
Benchmarking of DL Libraries and Models on Mobile Devices
Qiyang Zhang, Xiang Li, Xiangying Che +6
Deploying deep learning (DL) on mobile devices has been a notable trend in recent years. To support fast inference of on-device DL, DL libraries play a critical role as algorithms…