JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
A Federated Multi-Task Learning Model Based on Adaptive Distributed Data Latent Correlation Analysis |
| 영문제목(English Title) |
A Federated Multi-Task Learning Model Based on Adaptive Distributed Data Latent Correlation Analysis |
| 저자(Author) |
Shengbin Wu
Yibai Wang
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| 원문수록처(Citation) |
VOL 17 NO. 03 PP. 0441 ~ 0452 (2021. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task’s similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations. |
| 키워드(Keyword) |
Data Distribution
Federated Multi-Task Learning
Rank Constraint
Underlying Structure
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| 파일첨부 |
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