Evaluation of Federated Learning Aggregation Algorithms

被引:36
作者
Ek, Sannara [1 ]
Portet, Francois [1 ]
Lalanda, Philippe [1 ]
Vega, German [1 ]
机构
[1] Grenoble Alpes Univ, Grenoble, France
来源
UBICOMP/ISWC '20 ADJUNCT: PROCEEDINGS OF THE 2020 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2020 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS | 2020年
关键词
Federated Learning; Edge Computing; Human activity recognition; HUMAN ACTIVITY RECOGNITION;
D O I
10.1145/3410530.3414321
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Pervasive computing promotes the integration of connected electronic devices in our living spaces in order to assist us through appropriate services. Two major developments have gained significant momentum recently: a better use of fog resources and the use of AI techniques. Specifically, interest in machine learning approaches for engineering applications has increased rapidly. \ This paradigm seems to fit the pervasive environment well. However, federated learning has been applied so far to specific services and remains largely conceptual. It needs to be tested extensively on pervasive services partially located in the fog. In this paper, we present experiments performed in the domain of Human Activity Recognition on smartphones in order to evaluate existing algorithms.
引用
收藏
页码:638 / 643
页数:6
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