most citedLotto: Secure Participant Selection against Adversarial Servers in Federated Learning

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cs.DC2025

Janus: Disaggregating Attention and Experts for Scalable MoE Inference

Zhexiang Zhang, Ye Wang, Yumiao Zhao +9

Serving large Mixture-of-Experts (MoE) models is challenging because of their large memory footprints, heterogeneous resource demands, and highly dynamic inference workloads. Most…

cs.DC2025

λScale: Enabling Fast Scaling for Serverless Large Language Model Inference

Minchen Yu, Rui Yang, Chaobo Jia +9

Serverless computing has emerged as a compelling solution for cloud-based model inference. However, as modern large language models (LLMs) continue to grow in size, existing server…