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Mix-or-Split schedules LLM inference across devices, edge and cloud using a fine-grained latency model.

Joint caching of large models with parameter sharing and inference offloading in mobile edge computing.

DIAMM splits multimodal model inference across edge devices to cut latency.

CeeTrain speeds up geo-distributed model training across mixed GPUs and networks spanning cloud, edge and devices.

Uses contextual sparsity to get around the memory bandwidth limit in edge LLM inference.

Proposes serving RAG workloads through decentralized collaboration of edge devices instead of the cloud.

Deep reinforcement learning task scheduling for mobile edge computing that accounts for providers overselling capacity.

Uses curiosity-driven reinforcement learning to schedule requests collaboratively across mobile edge and cloud servers.

Schedules dependent tasks and manages container image layers together across device, edge and cloud tiers.

A Stackelberg game plus diffusion-based auction for offloading agentic AI tasks in IoT.