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20172021
most citedNo Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

39 citations · 221 across the 21 of their papers we have counts for

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7 papers · 1 filter

cs.LG2021★ 19 cited

Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning

Sen Cui, Weishen Pan, Jian Liang +2

Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples acro…

cs.LG2021

Collaboration Equilibrium in Federated Learning

Sen Cui, Jian Liang, Weishen Pan +3

Federated learning (FL) refers to the paradigm of learning models over a collaborative research network involving multiple clients without sacrificing privacy. Recently, there have…

cs.LG2021★ 39 cited

No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

Mi Luo, Fei Chen, Dapeng Hu +3

A central challenge in training classification models in the real-world federated system is learning with non-IID data. To cope with this, most of the existing works involve enforc…

cs.LG2020★ 22 cited

Hybrid Differentially Private Federated Learning on Vertically Partitioned Data

Chang Wang, Jian Liang, Mingkai Huang +3

We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized l…

cs.LG2020★ 24 cited

Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning

Yinghua Zhang, Yangqiu Song, Jian Liang +2

Transfer learning has become a common practice for training deep learning models with limited labeled data in a target domain. On the other hand, deep models are vulnerable to adve…

cs.LG2020

Relation-Guided Representation Learning

Zhao Kang, Xiao Lu, Jian Liang +2

Deep auto-encoders (DAEs) have achieved great success in learning data representations via the powerful representability of neural networks. But most DAEs only focus on the most do…

cs.LG2020★ 3 cited

General-Purpose User Embeddings based on Mobile App Usage

Junqi Zhang, Bing Bai, Ye Lin +3

In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage. User behaviors on mobile app usage, including retention, installation, and unin…