A Novel Personalized Federated Learning Method for Privacy-Preserving Smart Mobile Health Monitoring

References

• Adday BN, Shaker K, Salman I, Shaker H (2025) Parkinson’s disease detection using deep learning approach based on wearable sensor-based daily monitoring. Mesopotamian J. Artificial Intelligence Healthcare 2025:26–38.Crossref, Google Scholar
• Albaseer A, Abdallah M, Al-Fuqaha A, Erbad A (2023) Data-driven participant selection and bandwidth allocation for heterogeneous federated edge learning. IEEE Trans. Systems Man Cybernetics Systems 53(9):5848–5860.Crossref, Google Scholar
• Aleksovski D, Miljkovic D, Bravi D, Antonini A (2018) Disease progression in Parkinson subtypes: The PPMI dataset. Neurology Sci. 39:1971–1976.Crossref, Google Scholar
• Anter AM, Mohamed AW, Zhang M, Zhang Z (2023) A robust intelligence regression model for monitoring Parkinson’s disease based on speech signals. Future Generation Comput. Systems 147:316–327.Crossref, Google Scholar
• Apple Support (2024) Manage health data on your iPhone, iPad, or Apple Watch. Accessed December 6, 2024, https://support.apple.com/en-us/108779.Google Scholar
• Briggs C, Fan Z, Andras P (2020) Federated learning with hierarchical clustering of local updates to improve training on non-IID data. 2020 Internat. Joint Conf. Neural Networks (IJCNN) (IEEE, Piscataway, NJ), 1–9.Google Scholar
• Chai Y, Liu H, Liu X, Wang L, Liu Y, Wang J (2025) A novel personalized federated learning method for privacy-preserving smart mobile health monitoring. https://doi.org/10.1287/ijoc.2023.0521.cd, https://github.com/INFORMSJoC/2023.0521.Google Scholar
• Chen OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, Lindemann M, De Vos M (2020) Building a machine-learning framework to remotely assess Parkinson’s disease using smartphones. IEEE Trans. Biomedical Engrg. 67(12):3491–3500.Crossref, Google Scholar
• Coats H, Downey L, Sharma RK, Curtis JR, Engelberg RA (2018) Quality of communication and trust in patients with serious illness: An exploratory study of the relationships of race/ethnicity, socioeconomic status, and religiosity. J. Pain Symptom Management 56(4):530–540.Crossref, Google Scholar
• Doshi K, Yilmaz Y (2023) Privacy-preserving video understanding via transformer-based federated learning. 2023 IEEE Conf. Dependable Secure Comput. (DSC) (IEEE, Piscataway, NJ), 1–8.Google Scholar
• Gupta D, Kayode O, Bhatt S, Gupta M, Tosun AS (2021) Hierarchical federated learning based anomaly detection using digital twins for smart healthcare. IEEE 7th Int. Conf. Collaboration Internet Comput. (CIC) (IEEE, Piscataway, NJ), 16–25.Google Scholar
• Habib Z, Mughal MA, Khan MA, Hamza A, Alturki N, Jamel L (2024) A novel deep dual self-attention and Bi-LSTM fusion framework for Parkinson’s disease prediction using freezing of gait: A biometric application. Multimedia Tools Appl. 83:80179–80200.Crossref, Google Scholar
• Huang W, Zhang L, Gao W, Min F, He J (2021a) Shallow convolutional neural networks for human activity recognition using wearable sensors. IEEE Trans. Instrumentation Measurement 70:1–11.Google Scholar
• Huang H, Hu Z, Wang Y, Lu Z, Wen X, Fu B (2023) Train a central traffic prediction model using local data: A spatio-temporal network based on federated learning. Engrg. Appl. Artificial Intelligence 125:106612.Crossref, Google Scholar
• Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, Zhang Y (2021b) Personalized cross-silo federated learning on non-IID data. Proc. AAAI Conf. Artificial Intelligence 35(9):7865–7873.Crossref, Google Scholar
• International Association of Privacy Professionals (2011) IAPP information privacy certification: Glossary of common privacy terminology. Accessed December 6, 2024, https://iapp.org/media/pdf/certification/CIPP_Glossary_0211updated.pdf.Google Scholar
• Khan AR, Manzoor HU, Ayaz F, Imran MA, Zoha A (2023) A privacy and energy-aware federated framework for human activity recognition. Sensors 23(23):9339.Crossref, Google Scholar
• Lakhan A, Mohammed MA, Abdulkareem KH, Hamouda H, Alyahya S (2023) Autism spectrum disorder detection framework for children based on federated learning integrated CNN-LSTM. Comput. Biol. Medicine 166:107539.Crossref, Google Scholar
• Lamichhane B, Zhou J, Sano A (2023) Psychotic relapse prediction in schizophrenia patients using a personalized mobile sensing-based supervised deep learning model. IEEE J. Biomedical Health Inform. 27(7):3246–3257.Crossref, Google Scholar
• Li M, Tang X (2024) FLEER: A federated learning framework for EEG emotion recognition. 2024 5th Internat. Seminar Artificial Intelligence Networking Inform. Tech. (AINIT) (IEEE, Piscataway, NJ), 285–288.Google Scholar
• Li X, Huang K, Yang W, Wang S, Zhang Z (2019) On the convergence of FedAvg on non-IID data. Preprint, submitted July 4, https://arxiv.org/abs/1907.02189.Google Scholar
• Li Z, Fu Y, Tian M, Li C, Yu FR, Cheng N (2025) FedSTDN: A federated learning-enabled spatial-temporal prediction model for wireless traffic prediction. IEEE Trans. Mobile Comput. 24(9):8945–8958.Crossref, Google Scholar
• Liu S, Tang S, Zheng J, Ni LM (2022a) Unsupervised learning for human mobility behaviors. INFORMS J. Comput. 34(3):1565–1586.Link, Google Scholar
• Liu Q, Sun S, Liang Y, Xue J, Liu M (2024) Personalized federated learning for spatio-temporal forecasting: A dual semantic alignment-based contrastive approach. Preprint, submitted April 4, https://arxiv.org/abs/2404.03702.Google Scholar
• Liu Z, Khojandi A, Li X, Mohammed A, Davis RL, Kamaleswaran R (2022b) A machine learning–enabled partially observable Markov decision process framework for early sepsis prediction. INFORMS J. Comput. 34(4):2039–2057.Link, Google Scholar
• Lu W, Wang J, Chen Y, Qin X, Xu R, Dimitriadis D, Qin T (2022) Personalized federated learning with adaptive BatchNorm for healthcare. IEEE Trans. Big Data 10(6):915–925.Crossref, Google Scholar
• Misgar MM, Bhatia MPS (2024) Utilizing deep convolutional neural architecture with attention mechanism for objective diagnosis of schizophrenia using wearable IoMT devices. Multimedia Tools Appl. 83(13):39601–39620.Crossref, Google Scholar
• Nguyen DMD, Miah M, Bilodeau GA, Bouachir W (2022) Transformers for 1D signals in Parkinson’s disease detection from gait. 2022 26th Internat. Conf. Pattern Recognition (ICPR) (IEEE, Piscataway, NJ), 5089–5095.Google Scholar
• Park J, Lee K, Lee S, Zhang M, Ko J (2023) AttFL: A personalized federated learning framework for time-series mobile and embedded sensor data processing. Proc. ACM Interactive Mobile Wearable Ubiquitous Tech. 7(3):1–31.Crossref, Google Scholar
• Pham CH, Huynh-The T, Sedgh-Gooya E, El-Bouz M, Alfalou A (2024) Extension of physical activity recognition with 3D CNN using encrypted multiple sensory data to federated learning based on multi-key homomorphic encryption. Comput. Methods Programs Biomedicine 243:107854.Crossref, Google Scholar
• Pratap A, Atkins DC, Renn BN, Tanana MJ, Mooney SD, Anguera JA, Areán PA (2019) The accuracy of passive phone sensors in predicting daily mood. Depression Anxiety 36(1):72–81.Crossref, Google Scholar
• Presotto R, Civitarese G, Bettini C (2022) FedCLAR: Federated clustering for personalized sensor-based human activity recognition. 2022 IEEE Internat. Conf. Pervasive Comput. Commun. (PerCom) (IEEE, Piscataway, NJ), 227–236.Google Scholar
• Ray A, Jank W, Dutta K, Mullarkey M (2023) An LSTM+ model for managing epidemics: Using population mobility and vulnerability for forecasting COVID-19 hospital admissions. INFORMS J. Comput. 35(2):440–457.Link, Google Scholar
• Sachin DN, Annappa B, Ambesenge S (2022) Federated learning for wearable sensor-based human activity recognition. Arya KV, Tripathi VK, Rodriguez C, Yusuf E, eds. Proc. 7th ASRES Internat. Conf. Intelligent Tech. ICIT 2022, Lecture Notes in Networks and Systems, vol. 685 (Springer, Singapore), 131–139.Google Scholar
• Satariano A (2019) Google is fined $57 million under Europe’s data privacy law. New York Times (January 21), https://www.nytimes.com/2019/01/21/technology/google-europe-gdpr-fine.html.Google Scholar
• Shui X, Xu H, Tan S, Zhang D (2025) Depression recognition using daily wearable-derived physiological data. Sensors 25(2):567.Crossref, Google Scholar
• Srivastava A, Suresh T, Lord SP, Akhtar MS, Chakraborty T (2022) Counseling summarization using mental health knowledge guided utterance filtering. KDD’22: Proc. 28th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 3920–3930.Google Scholar
• Tsanas A, Little M, McSharry P, Ramig L (2009) Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests. Nature Precedings (Nature Publishing Group, London, UK), 1.Crossref, Google Scholar
• Tu L, Ouyang X, Zhou J, He Y, Xing G (2021) FedDL: Federated learning via dynamic layer sharing for human activity recognition. SenSys’21: Proc. 19th ACM Conf. Embedded Networked Sensor Systems (Association for Computing Machinery, New York), 15–28.Google Scholar
• Váradi C (2020) Clinical features of Parkinson’s disease: The evolution of critical symptoms. Biology 9(5):103.Crossref, Google Scholar
• Wang R, Aung MSH, Abdullah S, Brian R, Campbell AT, Choudhury T, Hauser M, Kane J, Merrill M, Scherer EA (2016) CrossCheck: Toward passive sensing and detection of mental health changes in people with schizophrenia. UbiComp’16: Proc. 2016 ACM Internat. Joint Conf. Pervasive Ubiquitous Comput. (Association for Computing Machinery, New York), 886–897.Google Scholar
• Wang R, Wang W, Aung MSH, Ben-Zeev D, Brian R, Campbell AT, Choudhury T, Hauser M, Kane J, Scherer EA (2017) Predicting symptom trajectories of schizophrenia using mobile sensing. Proc. ACM Interactive Mobile Wearable Ubiquitous Tech. 1(3):1–24.Google Scholar
• Xu J, Wang S, Wang L, Yao ACC (2021) FedCM: Federated learning with client-level momentum. Preprint, submitted June 21, https://arxiv.org/abs/2106.10874.Google Scholar
• Yang Q, Zhang J, Hao W, Spell GP, Carin L (2021) Flop: Federated learning on medical datasets using partial networks. KDD’21: Proc. 27th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 3845–3853.Google Scholar
• Ye R, Ni Z, Wu F, Chen S, Wang Y (2023) Personalized federated learning with inferred collaboration graphs. Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J, eds. Internat. Conf. Machine Learn. (PMLR, New York), 39801–39817.Google Scholar