The document proposes using federated learning to enhance early and accurate detection of Alzheimer's disease. It discusses how federated learning allows for collaborative data analysis while preserving privacy across diverse healthcare datasets. The document outlines a proposed model and workflow for federated learning. It then describes implementing the model on an MRI dataset to classify Alzheimer's images. Results showed the model achieved accurate classification while ensuring data is not shared but analyzed locally on devices.

1. Contents
• Introduction
• Objective
• Literature Review
• Problem Statement
• Proposed Model
• Implementation
• Results
• Conclusion
• References
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2. • Alzheimer is a progressive neurological disorder that affects brain cells,impacting memory loss, thinking and
behavioral changes.
• Affects millions worldwide, with a growing prevalence due to an aging population.
• Early and accurate diagnosis remains a significant hurdle for effective treatment and management.
• To enhance early and accurate detection of Alzheimer's Disease using advanced technological solutions like
Federated Learning.
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Introduction

3. • Create a precise model for early Alzheimer's detection and diagnosis.
• Use Federated Learning for collaborative data analysis.
• Ensure privacy across diverse healthcare datasets.
• Improve accuracy while preserving sensitive information.
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Objective

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Literature Review
Table 1 : Literature Review
Ref.No Publisher/
Year of
Publication
Technologies Used Objective Strong Point Weak Point
1. Nature/ 2020 Federated learning,
IoMT
Design a smart
healthcare system
using Federated
Learning and
Internet of Medical
Things
• Privacy-Preserving
Decentralized Learning.
• Enhanced Personalized
Healthcare Insights.
• Device Heterogeneity
Complexity.
• Security Risks in
Connectivity.
2. Springer/2022 Federated learning,
blockchain
Using blockchain
with federated
learning to enhance
data privacy, security
and trust by ensuring
decentralized control
of data.
• Improved Security
Through Decentralization.
• Blockchain Trust for
Transparency
• Complex Integration
and Implementation.
• Potential Scalability
Challenges.

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Table 1 : Literature Review
Ref.No Publisher/
Year of
Publication
Technologies Used Objective Strong Point Weak Point
3. IEEE/ 2022 Federated learning,
Homomorphic
encryption and
Internet of Things
Using Homomorphic
encryption based
privacy preserving
federated learning in
IOT-enabled
healthcare system.
• Robust Privacy Protection
Mechanism.
• Secure Federated Learning
Framework.
• Homomorphic
Encryption Complexity.
• Limited Processing
Power Constraint.
4. IEEE/ 2022 Federated learning,
blockchain and
IoMT
Building a Federated
learning based
privacy preservation,
fraud detection and
blockchain IoMT
system for
healthcare.
• Personalized insights
without compromising
privacy.
• Ensuring integrity and
transparent transactions.
• Data flow challenges
affecting performance.
• High resource
demands during
deployment.

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Table 1 : Literature Review
Ref.No Publisher/
Year of
Publication
Technologies Used Objective Strong Point Weak Point
5. IEEE/2021 Federated learning Federated Learning
via Conditional
Mutual Learning for
Alzheimer’s Disease
Classification on
T1w MRI
• High structural detail for
brain analysis
• Established imaging
technique in Alzheimer's
diagnosis.
• Communication
Overhead
• Data Heterogeneity
6. MDPI/ 2023 Federated learning A Federated
Learning Model
Based on Hardware
Acceleration for the
Early Detection of
Alzheimer’s Disease
•Accelerated computation .
•Scalable.
• Dependency on
specific hardware
configurations

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Problem Statement
 "Developing an efficient and privacy-preserving method using federated learning for the accurate detection
and diagnosis of Alzheimer's disease, overcoming data fragmentation and privacy concerns in healthcare
datasets."

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Proposed Model
Fig 1: Model Diagram

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Federated Learning Workflow Diagram
Fig 2: Federated Learning Workflow Diagram

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The workflow of Federated Learning involves the following steps-
1. Initialization: Central server initializes a global model for federated learning.
2. Participant Selection: Edge devices, like smartphones or IoT devices, chosen for participation.
3. Model Distribution: Global model distributed to selected edge devices for local training.
4. Local Training: Edge devices train locally, using the global model as a starting point.
5. Model Update: Edge devices generate updates (gradients, weights) after local training.
6. Model Aggregation: Central server collects and aggregates updates to update the global model.
7. Iteration: Repeat steps 4-6 for multiple rounds to refine the global model iteratively.
8. Model Evaluation: Periodically assess the global model's accuracy and performance.
9. Deployment: Deploy the global model for inference while retaining it on edge devices.
10. Continuous Learning: System operates in a loop, periodically retraining the global model with the latest
data for continual adaptation to new patterns.

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Implementation
Dataset
 We have used a dataset from Kaggle called “Alzheimer MRI Dataset” in our project.
 The Dataset consists of MRI (Magnetic Resonance Imaging) Images.
 All the images are resized into 128 x 128 pixels.
 The Dataset consists of 6400 MRI images.
 The Dataset has four classes of images-
 Class - 1: Mild Demented (896 images)
 Class - 2: Moderate Demented (64 images)
 Class - 3: Non Demented (3200 images)
 Class - 4: Very Mild Demented (2240 images)

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Implementation (Cont.…)
Fig 3: Mild Demented Brain MRI Samples
Fig 4: Moderate Demented Brain MRI Samples

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Implementation (Cont.…)
Fig 5: Non Demented Brain MRI Samples
Fig 6: Very Mild Demented Brain MRI Samples

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Implementation (Cont.…)
Fig 8 : Loss and Accuracy

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Result
Fig 7: Comparing Predicted Classes with the Actual Classes from the Test Data

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Conclusion
• Federated Learning can revolutionize early Alzheimer's detection while ensuring data privacy.
• With its collaborative data analysis approach, it safeguards personal information.
• This method has the potential to transform how we diagnose and manage Alzheimer's.
• Ultimately, it may lead to improved treatments and better care strategies for those affected.

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References
1. Nicola Rieke, Jonny Hancox, Wenqi Li, Fausto Milletarì, Holger R. Roth, Shadi Albarqouni, Spyridon
Bakas, Mathieu N. Galtier, Bennett A. Landman, Klaus Maier-Hein, Sébastien Ourselin, Micah Sheller,
Ronald M. Summers, Andrew Trask, Daguang Xu, Maximilian Baust & M. Jorge Cardoso. The future of
digital health with federated learning. Nature/ 2020.
2. Attia Qammar, Ahmad Karim, Huansheng Ning & Jianguo Ding. Securing federated learning with
blockchain: a systematic literature review. Springer/2022.
3. Li Zhang, Jianbo Xu, Pandi Vijayakumar, Pradip Kumar Sharma, Uttam Ghosh. Homomorphic Encryption-
Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System. IEEE/ 2022.
4. Abdullah Lakhan, Mazin Abed Mohammed, Jan Nedoma, Radek Martinek. Federated-Learning Based
Privacy Preservation and Fraud-Enabled Blockchain IoMT System for Healthcare. IEEE/ 2022.
5. Ya-Lin Huang; Hao-Chun Yang; Chi-Chun Lee. Federated Learning via Conditional Mutual Learning for
Alzheimer’s Disease Classification on T1w MRI. IEEE/ 2021.
6. Kasem Khalil ,Mohammad Mahbubur Rahman Khan Mamun , Ahmed Sherif, Mohamed Said, Ahmad
Abdel-Aliem Imam. MDPI/ 2023.

18. Thank You
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