Federated Multi-Task Attention for Cross-Individual Human Activity Recognition
Authors: Qiang Shen, Haotian Feng, Rui Song, Stefano Teso, Fausto Giunchiglia, Hao Xu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments based on publicly available HAR datasets, which are collected in both controlled environments and real-world scenarios. Numeric results verify that our proposed Fed MAT significantly outperforms baselines not only in generalizing to existing individuals but also in adapting to new individuals. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Jilin University 2School of Artificial Intelligence, Jilin University 3University of Trento {shenqiang19, fenght21, songrui20}@mails.jlu.edu.cn, {fausto.giunchiglia, stefano.teso}@unitn.it, xuhao@jlu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Fed MAT. Input: m individual-specific data sets {Du}, one per client. Output: central model Θc, individual-specific models {Wu}. |
| Open Source Code | Yes | We open source Smart JLU dataset and source code on Github: https://github.com/Super-Shen/Fed MAT. |
| Open Datasets | Yes | HHAR [Stisen et al., 2015]: It contains 43, 930, 257 accelerometer and gyroscope recordings collected from 9 individuals performing 6 activities. PAMAP2 [Reiss and Stricker, 2012]: It contains 3, 850, 505 recordings from three inertial measurement units (IMUs) located on the hand, chest, and ankle. Extra Sensory [Vaizman et al., 2017]: It contains over 300, 000 instances labeled with 51 types of human contexts and collected in a natural environment from 60 individuals. Smart JLU:1 A similar dataset using the same tool and techniques as this one [Bison et al., 2021] collected in China, which contains over 30, 000 instances labeled with daily activities collected from 50 individuals, over two weeks in a real-life scenario in which participants are required to use their smartphones naturally. Footnote 1: We open source Smart JLU dataset and source code on Github: https://github.com/Super-Shen/Fed MAT. |
| Dataset Splits | Yes | For each user, we split the local dataset of each individual into a train set (80%) and a test set (20%). We apply the settings of meta-learning by splitting all the users in a dataset into meta-train users, which participate in the meta-learning process, and meta-test users for testing the meta-learned model. For Extra Sensory dataset, seven activities are selected and nine individuals are randomly selected as meta-train set, while one individual is selected as meta-test set. For Smart JLU, nine individuals are randomly selected as meta-train, while two individuals for meta-testing. ... We applied leave-one-individual-out validation. |
| Hardware Specification | Yes | All experiments are carried out on a machine with 2 NVIDIA Ge Force RTX 3090 GPUs. |
| Software Dependencies | Yes | We implemented Fed MAT using Python 3.6 and Pytorch 1.8. |
| Experiment Setup | Yes | The Adam optimizer with β1 = 0.9, β2 = 0.98, and ε = 10 8 is used to update all network parameters. For federated learning, we set λ = 1.0 and perform n = 10 epochs of local training at each update round. |