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Found 7 papers, 4 papers with code
Flexible Communication for Optimal Distributed Learning over Unpredictable Networks
Gradient compression alleviates expensive communication in distributed deep learning by sending fewer values and its corresponding indices, typically via Allgather (AG).
Accelerating Distributed ML Training via Selective Synchronization
In distributed training, deep neural networks (DNNs) are launched over multiple workers concurrently and aggregate their local updates on each step in bulk-synchronous parallel (BSP) training.
GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training
Distributed data-parallel (DDP) training improves overall application throughput as multiple devices train on a subset of data and aggregate updates to produce a globally shared model.
QTrojan: A Circuit Backdoor Against Quantum Neural Networks
We propose a circuit-level backdoor attack, \textit{QTrojan}, against Quantum Neural Networks (QNNs) in this paper.
Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility
Autonomous mobility is emerging as a new disruptive mode of urban transportation for moving cargo and passengers.
ScaDLES: Scalable Deep Learning over Streaming data at the Edge
In this paper, we introduce ScaDLES to efficiently train on streaming data at the edge in an online fashion, while also addressing the challenges of limited bandwidth and training with non-IID data.
CoCo Games: Graphical Game-Theoretic Swarm Control for Communication-Aware Coverage
We propose a novel framework for real-time communication-aware coverage control in networked robot swarms.
