4 research outputs found
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of
federated learning, an influx of approaches have delivered towards different
realistic challenges. In this survey, we provide a systematic overview of the
important and recent developments of research on federated learning. Firstly,
we introduce the study history and terminology definition of this area. Then,
we comprehensively review three basic lines of research: generalization,
robustness, and fairness, by introducing their respective background concepts,
task settings, and main challenges. We also offer a detailed overview of
representative literature on both methods and datasets. We further benchmark
the reviewed methods on several well-known datasets. Finally, we point out
several open issues in this field and suggest opportunities for further
research. We also provide a public website to continuously track developments
in this fast advancing field: https://github.com/WenkeHuang/MarsFL.Comment: 22 pages, 4 figure
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments