7 papers
Democratizing LLM Training with the Edge: Challenges, Systems, and the Road Ahead
Ziyue Luo, Jiaxuan Cai, Cedric LeDenmat +27
The growing computational power of modern GPU data centers has let a few industrial giants train and deploy large language models (LLMs) on massive amounts of data. Yet the sheer s…
CORE-RL: Confidence-Oriented Reliability Evaluation of Black-Box Reinforcement Learning Policies
Santhosh GS, Ananya Ravi, Devika Jay +5
The deployment of Reinforcement Learning (RL) agents in critical domains must be preceded with a pipeline to evaluate the alignment of the RL agent with complex multi-objective spe…
Federated Learning of Binary Neural Networks: Enabling Low-Cost Inference
Nitin Priyadarshini Shankar, Soham Lahiri, Sheetal Kalyani +1
Federated Learning (FL) preserves privacy by distributing training across devices. However, using DNNs is computationally intensive at the low-powered edge during inference. Edge d…
Distributed Perceptron under Bounded Staleness, Partial Participation, and Noisy Communication
Keval Jain, Anant Raj, Saurav Prakash +1
We study a semi-asynchronous client-server perceptron trained via iterative parameter mixing (IPM-style averaging): clients run local perceptron updates and a server forms a global…
SWAN: Sparse Winnowed Attention for Reduced Inference Memory via Decompression-Free KV-Cache Compression
Santhosh G S, Saurav Prakash, Balaraman Ravindran
Large Language Models (LLMs) face a significant bottleneck during autoregressive inference due to the massive memory footprint of the Key-Value (KV) cache. Existing compression tec…
AQUA: Attention via QUery mAgnitudes for Memory and Compute Efficient Inference in LLMs
Santhosh G S, Saurav Prakash, Balaraman Ravindran
The quadratic complexity of the attention mechanism remains a fundamental barrier to scaling Large Language Models (LLMs) to longer contexts, creating a critical bottleneck in both…
Federated Nonlinear System Identification
Omkar Tupe, Max Hartman, Lav R. Varshney +1
We consider federated learning of linearly-parameterized nonlinear systems. We establish theoretical guarantees on the effectiveness of federated nonlinear system identification co…