mlsanityData to 14 Oct 2026

Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems

arXiv 2405.05611 ↗First posted 9 May 2024cs.LG cs.CRcited by 3

Not on the map. Why?

Abstract (arXiv)

Machine Learning (ML) algorithms are generally designed for scenarios in which all data is stored in one data center, where the training is performed. However, in many applications, e.g., in the healthcare domain, the training data is distributed among several entities, e.g., different hospitals or patients' mobile devices/sensors. At the same time, transferring the data to a central location for learning is certainly not an option, due to privacy concerns and legal issues, and in certain cases, because of the communication and computation overheads. Federated Learning (FL) is the state-of-the-art collaborative ML approach for training an ML model across multiple parties holding local data samples, without sharing them. However, enabling learning from distributed data over such edge Internet of Things (IoT) systems (e.g., mobile-health and wearable technologies, involving sensitive personal/medical data) in a privacy-preserving fashion presents a major challenge mainly due to their stringent resource constraints, i.e., limited computing capacity, communication bandwidth, memory storage, and battery lifetime. In this paper, we propose a privacy-preserving edge FL framework for resource-constrained mobile-health and wearable technologies over the IoT infrastructure. We evaluate our proposed framework extensively and provide the implementation of our technique on Amazon's AWS cloud platform based on the seizure detection application in epilepsy monitoring using wearable technologies.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 31 of its 122 references that are in the map. First posted on arXiv 9 May 2024.

Builds on

31 of 122 references.

References · 31

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2019Advances and Open Problems in Federated Learningcited by 3,046
  6. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  7. 2018How To Backdoor Federated Learningcited by 765
  8. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  9. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  10. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
  11. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  12. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
19 more
  1. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  2. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 334
  3. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  4. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 210
  5. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  6. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  7. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 166
  8. 2019Automatic diagnosis of the 12-lead ECG using a deep neural networkcited by 164
  9. 2019Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulationcited by 141
  10. 2022Truth Serum: Poisoning Machine Learning Models to Reveal Their Secretscited by 101
  11. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  12. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  13. 2019Practical Federated Gradient Boosting Decision Treescited by 54
  14. 2019Federated Forestcited by 49
  15. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  16. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  17. 2021Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Applicationcited by 6
  18. 2022LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimationcited by 3
  19. 2023Model-Agnostic Federated Learningcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025EdgeWisePersona: A Dataset for On-Device User Profiling from Natural Language Interactionscited by 2
  2. 2025Federated Learning for Epileptic Seizure Prediction Across Heterogeneous EEG Datasetscited by 0
  3. 2026Toward Personalized Sleep Guidance from Wearable Data Using Language Modelscited by 0