This study delves into large PFMs adaptation in the resource-constrained federated learning environment, and proposes an innovative framework, namely ADRAP, which alternates between large model distillation and resource-adaptive pruning, with guaranteed convergence.
Xiao Zhang, Yang-Yang Wang, Xing-Yu Sun et al.· IEEE Transactions on Pattern...· 0 citations
NLCC, a node-level congestion control framework that configures a set of static CCA parameters shared by flows on a node and regulates the aggregate sending rate of a node, is proposed and applied to BBR and evaluated in CDN production environments.
Peng-Qiang Bi, Meng-Bai Xiao, Guang-Hui Zhang et al.· Proceedings of the ACM on Ne...· 0 citations
Modern large language model (LLM) inference suffers from severe Time-To-First-Token (TTFT) bottlenecks. Existing prefix KV caching mechanisms are inherently stateless, forcing a trade-off between cross-chunk attention accuracy and online recomputation overhead. To address this issue, we propose Pegasus, a novel statefu...
Fahao Chen, Peng Li, Dongxiao Yu et al.· Fall Joint Computer Conferen...· 0 citations
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