OpenMem: Hardware/Software Cooperative Management for Mobile Memory System

Wen, Fei; Qin, Mian; Gratz, Paul V.; Reddy, Narasimha

doi:10.1109/dac18074.2021.9586186

Cited by 9 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Incremental learning is designed for models built on streaming data to mitigate the catastrophic forgetting problem [14] by continuously updating the models with knowledge transfer. It also helps the model continuously monitor users' health states, while adapt to computation and memory constraints in mobile devices [13,15].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Graph Instance Transformer for Mental Health Inference

Dong

Tang

Cai

et al. 2021

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)

View full text Add to dashboard Cite

Mobile sensing appears as a promising solution for health inference problem (e.g., influenza-like symptom recognition) by leveraging diverse smart sensors to capture fine-grained information about human behaviors and ambient contexts. Centralized training of machine learning models can place mobile users' sensitive information under privacy risks due to data breach and misexploitation. Federated Learning (FL) enables mobile devices to collaboratively learn global models without the exposure of local private data. However, there are challenges of on-device FL deployment using mobile sensing: 1) long-term and continuously collected mobile sensing data may exhibit domain shifts as sensing objects (e.g. humans) have varying behaviors as a result of internal and/or external stimulus; 2) model retraining using all available data may increase computation and memory burden; and 3) the sparsity of annotated crowd-sourced data causes supervised FL to lack robustness. In this work, we propose FedMobile, an incremental semi-supervised federated learning algorithm, to train models semi-supervisedly and incrementally in a decentralized online fashion. We evaluate FedMobile using a real-world mobile sensing dataset for influenza-like symptom recognition. Our empirical results show that FedMobile-trained models achieve the best results in comparison to the selected baseline methods.

show abstract

Section: Introductionmentioning

confidence: 99%