Search Results for author: Weiwei Lin

Found 10 papers, 1 papers with code

Achieving Personalized Federated Learning with Sparse Local Models

no code implementations27 Jan 2022 Tiansheng Huang, Shiwei Liu, Li Shen, Fengxiang He, Weiwei Lin, DaCheng Tao

To counter this issue, personalized FL (PFL) was proposed to produce dedicated local models for each individual user.

Personalized Federated Learning

On Heterogeneously Distributed Data, Sparsity Matters

no code implementations29 Sep 2021 Tiansheng Huang, Shiwei Liu, Li Shen, Fengxiang He, Weiwei Lin, DaCheng Tao

Federated learning (FL) is particularly vulnerable to heterogeneously distributed data, since a common global model in FL may not adapt to the heterogeneous data distribution of each user.

Personalized Federated Learning

Variation-Incentive Loss Re-weighting for Regression Analysis on Biased Data

no code implementations14 Sep 2021 Wentai Wu, Ligang He, Weiwei Lin

Both classification and regression tasks are susceptible to the biased distribution of training data.


Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply

no code implementations10 Feb 2021 Tiansheng Huang, Weiwei Lin, Xiaobin Hong, Xiumin Wang, Qingbo Wu, Rui Li, Ching-Hsien Hsu, Albert Y. Zomaya

With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery.


FedProf: Selective Federated Learning with Representation Profiling

1 code implementation2 Feb 2021 Wentai Wu, Ligang He, Weiwei Lin, Carsten Maple

The results show that the selective behaviour of our algorithm leads to a significant reduction in the number of communication rounds and the amount of time (up to 2. 4x speedup) for the global model to converge and also provides accuracy gain.

Federated Learning Privacy Preserving

Stochastic Client Selection for Federated Learning with Volatile Clients

no code implementations17 Nov 2020 Tiansheng Huang, Weiwei Lin, Li Shen, Keqin Li, Albert Y. Zomaya

Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public.

Fairness Federated Learning +1

An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee

no code implementations3 Nov 2020 Tiansheng Huang, Weiwei Lin, Wentai Wu, Ligang He, Keqin Li, Albert Y. Zomaya

The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.

Distributed Computing Fairness +1

SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead

no code implementations3 Oct 2019 Wentai Wu, Ligang He, Weiwei Lin, Rui Mao, Carsten Maple, Stephen Jarvis

Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence.

Federated Learning

Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality

no code implementations3 Aug 2019 Wentai Wu, Ligang He, Weiwei Lin, Yi Su, Yuhua Cui, Carsten Maple, Stephen Jarvis

In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data.

Line Detection Time Series +1

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