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cs.DC2026
FedOptima: Optimizing Resource Utilization in Federated Learning
Zihan Zhang, Leon Wong, Blesson Varghese
Federated learning (FL) systems facilitate distributed machine learning across a server and multiple devices. However, FL systems have low resource utilization on servers and devic…
cs.DC2026
Multi-DNN Inference of Sparse Models on Edge SoCs
Jiawei Luo, Di Wu, Simon Dobson +1
Modern edge applications increasingly require multi-DNN inference systems to execute tasks on heterogeneous processors, gaining performance from both concurrent execution and from…
cs.DC2025
Ampere: Communication-Efficient and High-Accuracy Split Federated Learning
Zihan Zhang, Leon Wong, Blesson Varghese
A Federated Learning (FL) system collaboratively trains neural networks across devices and a server but is limited by significant on-device computation costs. Split Federated Learn…