Search Results for author: Yongxin Tong

Found 11 papers, 3 papers with code

Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning

no code implementations6 Apr 2024 Yan Kang, Ziyao Ren, Lixin Fan, Linghua Yang, Yongxin Tong, Qiang Yang

This vulnerability may lead the current heuristic hyperparameter configuration of SecureBoost to a suboptimal trade-off between utility, privacy, and efficiency, which are pivotal elements toward a trustworthy federated learning system.

Bayesian Optimization Hyperparameter Optimization +2

HeteFedRec: Federated Recommender Systems with Model Heterogeneity

no code implementations24 Jul 2023 Wei Yuan, Liang Qu, Lizhen Cui, Yongxin Tong, Xiaofang Zhou, Hongzhi Yin

Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems.

Knowledge Distillation Recommendation Systems

SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning

no code implementations20 Jul 2023 Ziyao Ren, Yan Kang, Lixin Fan, Linghua Yang, Yongxin Tong, Qiang Yang

To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage.

Privacy Preserving Vertical Federated Learning

A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management

no code implementations5 Jun 2023 Liyue Chen, Jiangyi Fang, Zhe Yu, Yongxin Tong, Shaosheng Cao, Leye Wang

In this paper, we propose RegionGen, a data-driven region generation framework that can specify regions with key characteristics (e. g., good spatial semantic meaning and predictability) by modeling region generation as a multi-objective optimization problem.


Transferring Knowledge Distillation for Multilingual Social Event Detection

1 code implementation6 Aug 2021 Jiaqian Ren, Hao Peng, Lei Jiang, Jia Wu, Yongxin Tong, Lihong Wang, Xu Bai, Bo wang, Qiang Yang

Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.

Cross-Lingual Word Embeddings Event Detection +2

Pruning-Aware Merging for Efficient Multitask Inference

no code implementations23 May 2019 Xiaoxi He, Dawei Gao, Zimu Zhou, Yongxin Tong, Lothar Thiele

Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost.

Network Pruning

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