Search Results for author: Jianqing Zhang

Found 9 papers, 9 papers with code

FuseGen: PLM Fusion for Data-generation based Zero-shot Learning

1 code implementation18 Jun 2024 Tianyuan Zou, Yang Liu, Peng Li, Jianqing Zhang, Jingjing Liu, Ya-Qin Zhang

To mitigate such bias, we propose FuseGen, a novel data generation-based zero-shot learning framework that introduces a new criteria for subset selection from synthetic datasets via utilizing multiple PLMs and trained STMs.

Zero-Shot Learning

FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning

1 code implementation6 Jan 2024 Jianqing Zhang, Yang Liu, Yang Hua, Jian Cao

To reduce the high communication cost of transmitting model parameters, a major challenge in HtFL, prototype-based HtFL methods are proposed to solely share class representatives, a. k. a, prototypes, among heterogeneous clients while maintaining the privacy of clients' models.

Contrastive Learning Federated Learning

PFLlib: Personalized Federated Learning Algorithm Library

1 code implementation8 Dec 2023 Jianqing Zhang, Yang Liu, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Jian Cao

Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection, personalized FL (pFL) has gained significant prominence as a research direction within the FL domain.

Personalized Federated Learning

FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy

3 code implementations1 Jul 2023 Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan

To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively.

Personalized Federated Learning

FedALA: Adaptive Local Aggregation for Personalized Federated Learning

2 code implementations2 Dec 2022 Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client.

Personalized Federated Learning

TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation

1 code implementation16 Mar 2021 Jianqing Zhang, Dongjing Wang, Dongjin Yu

Especially, the attention mechanism in deep learning enables recommender systems to incorporate various features in an adaptive way.

Recommendation Systems

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