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Federated Learning and Blockchain-enabled Fog-IoT Platform for Wearables in Predictive Healthcare

Marc Baucas, Petros Spachos, Konstantinos Plataniotis

arXiv:2301.04511v1cs.LGcs.CRcs.NIeess.SP

TL;DR

Wearable IoT devices support predictive healthcare but raise challenges in patient-data privacy, predictive-service integrity, and network adaptability. The paper proposes a fog-IoT platform combining federated learning and private blockchain, then evaluates its implementation. The authors report feasibility in maintaining the HAR classifier’s integrity and addressing the platform’s security and adaptability challenges.

Abstract

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Over the years, the popularity and usage of wearable Internet of Things (IoT) devices in several healthcare services are increased. Among the services that benefit from the usage of such devices is predictive analysis, which can improve early diagnosis in e-health. However, due to the limitations of wearable IoT devices, challenges in data privacy, service integrity, and network structure adaptability arose. To address these concerns, we propose a platform using federated learning and private blockchain technology within a fog-IoT network. These technologies have privacy-preserving features securing data within the network. We utilized the fog-IoT network's distributive structure to create an adaptive network for wearable IoT devices. We designed a testbed to examine the proposed platform's ability to preserve the integrity of a classifier. According to experimental results, the introduced implementation can effectively preserve a patient's privacy and a predictive service's integrity. We further investigated the contributions of other technologies to the security and adaptability of the IoT network. Overall, we proved the feasibility of our platform in addressing significant security and privacy challenges of wearable IoT devices in predictive healthcare through analysis, simulation, and experimentation.

I. INTRODUCTION

Wearable IoT devices support remote predictive healthcare, but privacy, service-integrity, and network-adaptability concerns constrain their use. The paper proposes a fog-IoT platform combining federated learning and blockchain, and evaluates its predictive-service integrity with a testbed.

I. INTRODUCTION

  • Wearable IoT devices collect sensitive patient data, while limited processing, service-integrity concerns, and device diversity create privacy, reliability, and adaptability challenges.Heterogeneous devices also require ongoing medical-server maintenance and updates.Source: §I · p. 1
  • The proposed fog-IoT platform combines federated learning and blockchain to protect patient privacy, strengthen network security, and support decentralized servers and resource reallocation.The design targets network adaptability and sustainability alongside data protection.Source: §I · p. 1
  • A designed testbed simulates and evaluates the implementation, using model accuracy to examine whether the predictive service preserves classifier integrity.The paper also reports analysis, simulation, and experimentation as part of its evaluation.Source: §I · p. 1

II. BACKGROUND AND RELATED WORKS

Wearable IoT devices can support remote monitoring, early detection, and clinical decision-making, but their expanding use raises network security and data-privacy concerns. The paper combines cryptographic protection with machine learning and fog-based processing to address these concerns.

A. Benefits of Using Wearable IoT Devices in Healthcare

  • Wearable IoT devices enable remote monitoring and predictive analysis, supporting early detection and clinical decisions while potentially reducing hospital congestion and examination costs.The cited examples include remote heart monitoring and wearable sensors used for early information about health complications.Source: A. Benefits of Using Wearable IoT… · p. 2

B. Challenges of Using Wearable IoT Devices in Healthcare

  • Adding wearable endpoints expands potential attack surfaces and exposes sensitive transmitted patient information, increasing security and privacy concerns as networks grow.The section frames these concerns as a consequence of expanding the number of connected devices.Source: 1) Network Security and Data Privacy · p. 2

1) Network Security and Data Privacy:

  • Prior privacy strategies use multikeyword search for forward privacy or deep learning to separate private from raw data and reduce leakage risk.The paper describes its approach as combining cryptographic protection for data transmissions with machine-learning adaptability.Source: 1) Network Security and Data Privacy · p. 2
  • The proposed approach selects blockchain for encryption and federated learning for adaptive privacy preservation while keeping trained models optimized.The background section introduces the review of blockchain and federated-learning implementations for wearable healthcare IoT.Source: 1) Network Security and Data Privacy · p. 2, §II · p. 2

2) Data Integrity and Precision:

Reliable healthcare predictions depend on accurate, recent sensed information and precise data handling. To address data-integrity and network-adaptability concerns, the paper combines federated learning and blockchain with fog-based data management standardization.

2) Data Integrity and Precision:

  • Healthcare predictions depend on preserving sensed physiological information as recent and precise from the wearable device through transmission and aggregation.Errors in sensing, processing, collection, or storage can produce inaccurate patient evaluations.Source: 2) Data Integrity and Precision · p. 2
  • Prior integrity approaches use a cryptographic accumulator for secure wearable-data collection or blockchain security for healthcare IoT services.The cited works focus on cryptographic technologies to ensure sensed-data accuracy.Source: 2) Data Integrity and Precision · pp. 2–3
  • The proposed platform combines federated learning as a secure data aggregator with blockchain-based decentralized organization for accurate healthcare data analysis.Source: 2) Data Integrity and Precision · p. 3

3) Network Structure Adaptability and Flexibility:

  • Wearable-device diversity and ongoing technological change require healthcare networks to remain adaptable, since inflexible networks risk losing resources or service infrastructure.The section links the need for flexibility to continued changes in devices and healthcare technology.Source: 3) Network Structure Adaptability and… · p. 3
  • Rather than specialize wearable prototypes, the authors propose fog-based data-management standardization with blockchain and federated learning for a modular healthcare IoT network.The related approaches cited include wearable gateways designed to connect other devices.Source: 3) Network Structure Adaptability and… · p. 3

C. Federated Learning for Wearable IoT Devices

The section explains how federated learning distributes model training and how blockchain can add trusted access and tamper resistance. The proposed design combines these ideas with fog computing to accommodate wearable devices’ processing limits.

C. Federated Learning for Wearable IoT Devices

  • Federated learning has clients train on raw data and a server aggregate their models into a global model that is redistributed to clients.The global model is updated as clients provide new knowledge.Source: C. Federated Learning for Wearable IoT… · p. 3
  • Keeping analysis local means the server receives trained models rather than raw data, while local parallel training can reduce training time and data volume.The passage also describes earlier detection of new correlations and behavior changes as an advantage over centralized training.Source: C. Federated Learning for Wearable IoT… · p. 3
  • The proposed design combines federated learning with a fog-IoT network and blockchain, moving training to fog devices to account for wearables’ limited processing capabilities.The authors frame the resulting network as decentralized, secure, and sustainable for continuous healthcare sensing, learning, and analysis.Source: C. Federated Learning for Wearable IoT… · p. 3

D. Blockchain for Wearable IoT Devices

  • Blockchain provides distributed information storage, trusted access control, and resistance to external or unwanted modification.Prior wearable-healthcare examples use blockchain consent, device authentication, and searchable encryption to address data privacy and security.Source: D. Blockchain for Wearable IoT Devices · pp. 3–4
  • The authors choose a private blockchain because its trusted-device list avoids the processing demands of public-chain proof-of-work for low-end wearables.The paper describes the private chain as a hyperledger that regulates devices according to a network-owner-defined list of trusted devices.Source: D. Blockchain for Wearable IoT Devices · p. 4

III. DESIGN AND METHODOLOGY

The design uses federated learning and fog computing to distribute wearable-data processing, with private blockchain supporting secure client authorization and model tracking. Training is offloaded from resource-limited wearables to fog devices, and a server aggregates local models into a global model.

A. Architecture Overview

  • Federated learning is used to organize locally trained model data, aggregate knowledge, and provide a global model while keeping private information secure.The authors state that clients send locally trained models rather than raw data to the server.Source: A. Architecture Overview · p. 4
  • The authors describe the distributed design as scalable and flexible while aiming not to impede the service’s learning and analysis.The architecture overview identifies Fig. 1 as the network arrangement for the platform.Source: A. Architecture Overview · p. 4, Figure 1 · p. 4
  • The design combines federated learning with private blockchain for client authorization and uses the ledger to store client IDs and locally trained models for version tracking and debugging.The overview presents this tracking as supporting data analysis and learning in healthcare networks.Source: A. Architecture Overview · p. 4
  • The platform offloads local model training from low-end wearables to intermediary fog devices because training consumes substantial processing capacity.This is intended to support a diverse pool of commercial wearable devices.Source: A. Architecture Overview · p. 4
  • Fog devices collect wearable data and train locally; a server stores and tracks local models, aggregates them into a global model, and returns it through fog nodes.The returned model supports further data analysis on wearable devices.Source: A. Architecture Overview · p. 4

B. Dataset and Neural Network

The testbed uses a human-activity-recognition dataset and a one-dimensional CNN to examine the platform rather than maximize classifier accuracy. The authors select a documented dataset and limit CNN calibration to the epoch count to reduce experimental confounds.

B. Dataset and Neural Network

  • The classifier is a human activity recognition model using accelerometer data, selected to test platform security, service integrity, and flexibility rather than maximize accuracy.The intended service simulates patient data for remote monitoring, including movement and accident detection.Source: B. Dataset and Neural Network · p. 5
  • The UCI smartphone activity dataset contains 10,299 instances from 30 volunteers performing six activities, with 561 labeled features from accelerometer and gyroscope measurements.The volunteers were aged 19–48 and wore a Samsung Galaxy SII at the waist.Source: B. Dataset and Neural Network · p. 5
  • The authors implement a one-dimensional CNN for sequential single-dimensional input, use the dataset’s 70–30 train-test split, and calibrate only the epoch count.They state that these choices limit processing requirements and reduce impacts from classifier or data variation on the platform experiments.Source: B. Dataset and Neural Network · p. 5

C. Federated Learning Implementation

The implementation trains HAR models at fog devices and aggregates client models at a central server, scaling local weights according to accuracy and client count. A private blockchain authorizes devices and records model-training operations to support tracking and tamper resistance.

C. Federated Learning Implementation

  • The federated-learning objective is a global model that minimizes the weighted average of client losses, and the server script iterates over locally trained fog-device models.The passage defines each client’s local loss as Li.Source: C. Federated Learning Implementation · p. 5
  • The server scales local CNN weights according to client count and accuracy, gives the highest-accuracy model the largest scaling factor, then averages weights into a global CNN.Fig. 2 depicts the implemented federated-learning operations.Source: C. Federated Learning Implementation · p. 5, Figure 2 · p. 6

D. Private Blockchain Implementation

  • The private blockchain assigns device IDs for server-client authorization and stores locally trained models in blocks linked by hashes of prior blocks.The resulting structure logs model transmissions and other network operations.Source: D. Private Blockchain Implementation · p. 5
  • Copies of the blockchain are distributed to clients so the service can check client and local-model validity while keeping records decentralized.The passage states that the copies are intended to remain free from manipulation outside authorized devices.Source: D. Private Blockchain Implementation · p. 6
  • The authors report that the blockchain records local-training results and global-model changes, while raw client data are not sent to the server.They characterize the ledger as making tampering more difficult and securing network endpoints.Source: D. Private Blockchain Implementation · p. 6

E. Hardware and Software Specifications

The testbed uses low-cost Raspberry Pis to model fog and cloud servers and evaluates a federated classifier under constrained device capabilities. Ten fog nodes train locally and send models to a central server for aggregation.

IV. RESULTS AND DISCUSSIONS

  • The testbed assesses data-analysis security, classifier integrity, and flexibility while modeling fog-to-cloud data flow under minimal processing capabilities.The wearable-to-fog data transmissions are skipped because each fog server is assumed to already contain the data.Source: §IV · p. 6, A. Testbed · p. 6

A. Testbed

  • Ten fog Pis and one cloud Pi model the network, using identical low-cost devices to limit processing or data-capacity advantages.The authors selected Raspberry Pi 3 Bs and report that each could run classifier training and blockchain scripts.Source: A. Testbed · p. 6
  • Each fog node trains a local model and sends it to the server, which scales weights by accuracy, aggregates them, and applies them to a matching CNN.The highest-accuracy local model receives the highest scaling factor; the remaining models receive equal, lower factors.Source: A. Testbed · p. 6

E. Hardware and Software Specifications

B. Integrity Evaluation via Training Model Accuracy

The global model keeps pace with, and eventually exceeds, the average accuracy of local models as client count increases. The evaluation also reports local-model variation and training-curve fluctuations that the authors judge insufficient to invalidate the results.

B. Integrity Evaluation via Training Model Accuracy

  • One of 10 local fog-client models, trained on 70% and tested on the remaining 30% of the dataset with 10 epochs and batch size 8, achieved 90.43% testing accuracy.Fig. 4 shows sample training and validation accuracy for one local model yielding that testing accuracy.Source: B. Integrity Evaluation via Training… · p. 7, Figure 4 · p. 7
  • The validation loss spikes at epoch 6 and accuracy at epoch 5; fluctuations within ±2% of the median were judged too small to render the model inaccurate.The authors attribute the loss spike to learning-rate volatility and say it stabilizes without significantly affecting the model.Source: B. Integrity Evaluation via Training… · p. 7
  • Local models had final accuracies within ±1.28% of the median, and their average served as the benchmark for comparison with the global model.The authors state that this spread was not significant enough to invalidate training results.Source: B. Integrity Evaluation via Training… · p. 7
  • 91.75% is the global model's peak accuracy; as client count increases, it keeps up with and eventually outperforms average local-model accuracy.The authors attribute the improvement to a scaling scheme that gives the best-performing local model a higher factor.Source: Figure 6 · p. 8, B. Integrity Evaluation via Training… · p. 8

C. Security and Adaptability Analysis

The security and adaptability analysis describes how the platform's federated learning, blockchain, and fog architecture support data privacy and network flexibility. Its decentralization also supports clustering that reduces update time.

1) Security:

  • Federated learning keeps raw data local, while blockchain access control and cryptography restrict trusted communication and can reduce spoofing, impersonation, and DoS exposure.The passage frames these as security benefits of the design, based on observations from simulations and testing.Source: 1) Security · p. 8
  • Fog decentralization supports federated learning and blockchain, while uniform locally trained model packages reduce update strain and make model regeneration modular.The authors say this modularity permits using other classifiers and wearable-device groups, provided the classifier structure remains uniform.Source: 2) Adaptability · p. 8
  • Fog decentralization also allows better clustering and reduces the time required to update servers and devices.The passage introduces a heterogeneity equation based on the distribution of worker update time.Source: 1) Security · p. 8

2) Adaptability:

Fog-based worker clustering standardizes update times and is theoretically expected to reduce network heterogeneity; the resulting global-model accuracy is reported as unaffected by moving learning to the fog.

2) Adaptability:

  • Fog devices cluster wearables into workers, standardizing update times; the paper’s heterogeneity measure describes the spread of worker update times.In the standard cloud arrangement, each wearable is a worker, and device diversity is associated with high heterogeneity; the authors say higher H corresponds to less impact from update times.Source: 2) Adaptability · pp. 8–9
  • The platform is theoretically expected to lower network heterogeneity by reducing the impact of diverse wearable-device update times.The authors identify each fog device’s training and communication time as the update time in their experiments.Source: 2) Adaptability · p. 9
  • Global-model accuracy is reported as unaffected by shifting learning location, while simpler sensing-device processing supports a more adaptable network for diverse wearables.The authors say edge devices need only send data securely, provided the fog device knows what data it receives.Source: 2) Adaptability · p. 9

D. Future Work and Recommendations

The authors identify additional metrics and broader model and dataset testing as future work, and conclude that their implemented platform is feasible for wearable predictive healthcare.

D. Future Work and Recommendations

  • The authors propose testing propagation delay, server power consumption, and additional neural-network designs and datasets in future iterations.They connect these evaluations to mobility and reach, sustainability and uptime, and platform modularity across configurations.Source: D. Future Work and Recommendations · p. 9

V. CONCLUSION

  • The authors conclude that testing and evaluation indicate feasibility for addressing security, integrity, and adaptability challenges in wearable predictive healthcare.They report feasibility in maintaining HAR-classifier integrity and discuss low-cost, low-end fog and cloud servers.Source: §V · p. 9
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