Jianchun Liu

Enhancing Federated Learning with In-Cloud Unlabeled Data

2022 · 2022 IEEE 38th International Conference on Data Engineering (ICDE)

Federated learning (FL) has been widely applied to collaboratively train deep learning (DL) models on massive end devices (i.e., clients). Due to the limited storage capacity and high labeling cost, there are always insufficient data …

Adaptive Block-Wise Regularization and Knowledge Distillation for Enhancing Federated Learning

2023 · IEEE/ACM Transactions on Networking

Federated Learning (FL) is a distributed model training framework that allows multiple clients to collaborate on training a global model without disclosing their local data in edge computing (EC) environments. However, FL usually faces statistical …

Collaborative Speculative Inference for Efficient LLM Inference Serving

2025 · arXiv (Cornell University)

Speculative inference is a promising paradigm employing small speculative models (SSMs) as drafters to generate draft tokens, which are subsequently verified in parallel by the target large language model (LLM). This approach enhances the efficiency …