7 papers
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…
DriftGuard: Mitigating Asynchronous Data Drift in Federated Learning
Yizhou Han, Di Wu, Blesson Varghese
In real-world Federated Learning (FL) deployments, data distributions on devices that participate in training evolve over time. This leads to asynchronous data drift, where differe…
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…
Data-Free Pruning of Self-Attention Layers in LLMs
Dhananjay Saikumar, Blesson Varghese
Many self-attention sublayers in large language models (LLMs) can be removed with little to no loss. We attribute this to the Attention Suppression Hypothesis: during pre-training,…
Mosaic: Composite Projection Pruning for Resource-efficient LLMs
Bailey J. Eccles, Leon Wong, Blesson Varghese
Extensive compute and memory requirements limit the deployment of large language models (LLMs) on any hardware. Compression methods, such as pruning, can reduce model size, which i…
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…