Search Results for author: Jiaxun Lu

Found 7 papers, 0 papers with code

Mining Latent Relationships among Clients: Peer-to-peer Federated Learning with Adaptive Neighbor Matching

no code implementations23 Mar 2022 Zexi Li, Jiaxun Lu, Shuang Luo, Didi Zhu, Yunfeng Shao, Yinchuan Li, Zhimeng Zhang, Chao Wu

Moreover, results show that our method is much effective in mining latent cluster relationships under various heterogeneity without assuming the number of clusters and it is effective even under low communication budgets.

Federated Learning

How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning

no code implementations2 Dec 2021 Shuo Wan, Jiaxun Lu, Pingyi Fan, Yunfeng Shao, Chenghui Peng, Khaled B. Letaief

In this paper, we develop a vertical-horizontal federated learning (VHFL) process, where the global feature is shared with the agents in a procedure similar to that of vertical FL without any extra communication rounds.

Federated Learning

Unified Group Fairness on Federated Learning

no code implementations9 Nov 2021 Fengda Zhang, Kun Kuang, Yuxuan Liu, Long Chen, Chao Wu, Fei Wu, Jiaxun Lu, Yunfeng Shao, Jun Xiao

We validate the advantages of the FMDA-M algorithm with various kinds of distribution shift settings in experiments, and the results show that FMDA-M algorithm outperforms the existing fair FL algorithms on unified group fairness.

Fairness Federated Learning

Convergence Analysis and System Design for Federated Learning over Wireless Networks

no code implementations30 Apr 2021 Shuo Wan, Jiaxun Lu, Pingyi Fan, Yunfeng Shao, Chenghui Peng, Khaled B. Letaief

Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets.

Federated Learning

Model change detection with application to machine learning

no code implementations19 Nov 2018 Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli

The goal is to detect whether the change in the model is significant, i. e., whether the difference between the pre-change parameter and the post-change parameter $\|\theta-\theta'\|_2$ is larger than a pre-determined threshold $\rho$.

Change Detection

Active and Adaptive Sequential learning

no code implementations29 May 2018 Yuheng Bu, Jiaxun Lu, Venugopal V. Veeravalli

Furthermore, an estimator of the change in the learning problems using the active learning samples is constructed, which provides an adaptive sample size selection rule that guarantees the excess risk is bounded for sufficiently large number of time steps.

Active Learning

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