11 citations · 17 across the 13 of their papers we have counts for
9 papers · 1 filter
SkillSpec: Consensus-Gated Agent Skill Evolution via Representation Specialization
Huancheng Chen, Xiaodi Sun, Zhaoqiong Huang +4
Natural-language skills are textual procedural memories through which large language model (LLM) agents retain reusable task knowledge without updating model weights. Existing meth…
Foundation-Preserving Optimization in Generalized Eigenspace
Dongjun Kim, Adrian de Wynter, Huancheng Chen +2
While finetuning effectively adapts foundation models to specialized downstream tasks, it can degrade nontarget capabilities acquired during pretraining. Existing forgetting aware…
CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization
Seohyeon Cha, Huancheng Chen, Dongjun Kim +4
Post-training quantization (PTQ) enables efficient deployment of large language models by mapping pretrained weights to low-bit formats without retraining, typically using a small…
Task-Agnostic Federated Continual Learning via Replay-Free Gradient Projection
Seohyeon Cha, Huancheng Chen, Haris Vikalo
Federated continual learning (FCL) enables collaborative model training across distributed clients on sequentially arriving tasks without revisiting past data. However, existing ap…
Recovering Labels from Local Updates in Federated Learning
Huancheng Chen, Haris Vikalo
Gradient inversion (GI) attacks present a threat to the privacy of clients in federated learning (FL) by aiming to enable reconstruction of the clients' data from communicated mode…
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices
Huancheng Chen, Haris Vikalo
While federated learning (FL) systems often utilize quantization to battle communication and computational bottlenecks, they have heretofore been limited to deploying fixed-precisi…
Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning
Huancheng Chen, Haris Vikalo
Statistical heterogeneity of data present at client devices in a federated learning (FL) system renders the training of a global model in such systems difficult. Particularly chall…
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation
Huancheng Chen, Johnny, Wang +1
Heterogeneity of data distributed across clients limits the performance of global models trained through federated learning, especially in the settings with highly imbalanced class…
Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data
Huancheng Chen, Haris Vikalo
Federated learning (FL) is a privacy-promoting framework that enables potentially large number of clients to collaboratively train machine learning models. In a FL system, a server…