127 citations · 559 across the 76 of their papers we have counts for
13 papers · 1 filter
Unleashing the Potential of Stage-Wise Decision-Making in Scheduling of Graph-Structured Tasks over Mobile Vehicular Clouds
Minghui Liwang, Bingshuo Guo, Zhanxi Ma +5
To effectively process high volume of data across a fleet of dynamic and distributed vehicles, it is crucial to implement resource provisioning techniques that can provide reliable…
Distributed Learning over Networks with Graph-Attention-Based Personalization
Zhuojun Tian, Zhaoyang Zhang, Zhaohui Yang +2
In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model. However, due to the underlying non-i.i.d. data distribut…
Multi-Job Intelligent Scheduling with Cross-Device Federated Learning
Ji Liu, Juncheng Jia, Beichen Ma +5
Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine le…
RFID: Towards Low Latency and Reliable DAG Task Scheduling over Dynamic Vehicular Clouds
Zhang Liu, Minghui Liwang, Seyyedali Hosseinalipour +3
Vehicular cloud (VC) platforms integrate heterogeneous and distributed resources of moving vehicles to offer timely and cost-effective computing services. However, the dynamic natu…
FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server
Hong Zhang, Ji Liu, Juncheng Jia +3
Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. In this p…
Efficient Device Scheduling with Multi-Job Federated Learning
Chendi Zhou, Ji Liu, Juncheng Jia +4
Recent years have witnessed a large amount of decentralized data in multiple (edge) devices of end-users, while the aggregation of the decentralized data remains difficult for mach…