Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing

• URL: http://arxiv.org/abs/2312.12666v1
• Date: Tue, 19 Dec 2023 23:39:33 GMT
• Title: Incremental Semi-supervised Federated Learning for Health Inference via Mobile Sensing
• Authors: Guimin Dong, Lihua Cai, Mingyue Tang, Laura E. Barnes, and Mehdi Boukhechba
• Abstract summary: We propose FedMobile, an incremental semi-supervised federated learning algorithm. We evaluate FedMobile using a real-world mobile sensing dataset for influenza-like symptom recognition.
• Score: 5.434366992553875
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts [0.0]
  Social media posts provide valuable information about individuals' mental health conditions. In this study, we adopt Federated Learning (FL) to facilitate decentralized training on smartphones. To optimize the training process, we leverage a common tokenizer across all client devices.
  arXiv  Detail & Related papers  (2024-10-17T16:09:32Z)
• CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering [12.472038137777474]
  Human Activity Recognition (HAR) is vital for the automation and intelligent identification of human actions through data from diverse sensors. Traditional machine learning approaches by aggregating data on a central server and centralized processing are memory-intensive and raise privacy concerns. This work proposes CDFL, an efficient federated learning framework for image-based HAR.
  arXiv  Detail & Related papers  (2024-07-17T03:17:53Z)
• Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights [52.024964564408]
  This paper examines the added-value of implementing Federated Learning throughout all levels of the protocol stack. It presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry.
  arXiv  Detail & Related papers  (2023-12-07T20:39:57Z)
• Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
  We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
  arXiv  Detail & Related papers  (2023-10-21T12:07:04Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• Location Leakage in Federated Signal Maps [7.093808731951124]
  We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices. We formulate the problem within the online federated learning framework: (i) federated learning enables users to collaboratively train a model, while keeping their training data on their devices. We consider an honest-but-curious server, who observes the updates from target users participating in FL and infers their location using a deep leakage from gradients (DLG) type of attack. We build on this observation to protect location privacy, in our setting, by revisiting and designing mechanisms within the federated learning framework including: tuning the FL
  arXiv  Detail & Related papers  (2021-12-07T02:28:12Z)
• Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks [81.83990083088345]
  We develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks. Our analysis proves that the learning performance of HFL deteriorates drastically with highly-mobile users. To circumvent these issues, we propose a mobility-aware cluster federated learning (MACFL) algorithm.
  arXiv  Detail & Related papers  (2021-08-20T10:46:58Z)
• Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions [0.20236506875465865]
  Intrusion detection systems play a significant role in ensuring security and privacy of smart devices. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.
  arXiv  Detail & Related papers  (2021-06-16T13:13:04Z)
• FLaPS: Federated Learning and Privately Scaling [3.618133010429131]
  Federated learning (FL) is a distributed learning process where the model is transferred to the devices that posses data. We present Federated Learning and Privately Scaling (FLaPS) architecture, which improves scalability as well as the security and privacy of the system.
  arXiv  Detail & Related papers  (2020-09-13T14:20:17Z)
• WAFFLe: Weight Anonymized Factorization for Federated Learning [88.44939168851721]
  In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices. We propose Weight Anonymized Factorization for Federated Learning (WAFFLe), an approach that combines the Indian Buffet Process with a shared dictionary of weight factors for neural networks.
  arXiv  Detail & Related papers  (2020-08-13T04:26:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.