31 citations · 88 across the 12 of their papers we have counts for
5 papers · 1 filter
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts
Hanzi Mei, Dongqi Cai, Ao Zhou +2
As Large Language Models (LLMs) push the boundaries of AI capabilities, their demand for data is growing. Much of this data is private and distributed across edge devices, making F…
EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices
Rongjie Yi, Liwei Guo, Shiyun Wei +3
Large language models (LLMs) such as GPTs and Mixtral-8x7B have revolutionized machine intelligence due to their exceptional abilities in generic ML tasks. Transiting LLMs from dat…
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…
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…
Hierarchical Federated Learning through LAN-WAN Orchestration
Jinliang Yuan, Mengwei Xu, Xiao Ma +3
Federated learning (FL) was designed to enable mobile phones to collaboratively learn a global model without uploading their private data to a cloud server. However, exiting FL pro…