6 papers · 1 filter
Bridging On-Device and Cloud LLMs for Collaborative Reasoning: A Unified Methodology for Local Routing and Post-Training
Wenzhi Fang, Dong-Jun Han, Liangqi Yuan +2
Device-cloud collaboration holds promise for deploying large language models (LLMs), leveraging lightweight on-device models for efficiency while relying on powerful cloud models f…
Joint Continual Learning of Local Language Models and Cloud Offloading Decisions with Budget Constraints
Evan Chen, Wenzhi Fang, Shiqiang Wang +1
Locally deployed Small Language Models (SLMs) must continually support diverse tasks under strict memory and computation constraints, making selective reliance on cloud Large Langu…
Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization
Jianing Zhang, Evan Chen, Dong-Jun Han +2
Vertical Federated Learning (VFL) enables collaborative model training across feature-partitioned devices, yet its reliance on device-server information exchange introduces signifi…
Gradient Correction in Federated Learning with Adaptive Optimization
Evan Chen, Shiqiang Wang, Jianing Zhang +3
In federated learning (FL), model training performance is strongly impacted by data heterogeneity across clients. Client-drift compensation methods have recently emerged as a solut…
Hierarchical Federated Learning with Multi-Timescale Gradient Correction
Wenzhi Fang, Dong-Jun Han, Evan Chen +2
While traditional federated learning (FL) typically focuses on a star topology where clients are directly connected to a central server, real-world distributed systems often exhibi…
Differentially-Private Multi-Tier Federated Learning
Evan Chen, Frank Po-Chen Lin, Dong-Jun Han +1
While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from the communicated model parameters. In this wor…