This document discusses federated learning as a decentralized machine learning technique particularly beneficial for medical imaging, emphasizing data privacy and security. It outlines the challenges of traditional deep learning, such as data centralization and high computational costs, and highlights the benefits of federated learning in mitigating these issues. A literature survey on federated learning applications and frameworks is included, along with future directions for advancements and integration with other technologies.

1. FEDERATED LEARNING FOR
MEDICAL IMAGING
SUBMITTED BY :
Group No. 10
Guide Name – Dr Rashmi Ranjan Sahoo
Ankita Patra(2101109131)
Ansuman Simanta Shekar Bhujabala(2101109132)
Hritik Dash(2101109143)
Purnima Prusty(2101109185)

2. Introduction to
Federated
Learning
Federated learning is a machine learning technique that trains
models on decentralized data. It allows devices to collaborate
without sharing their data.

3. Limitations of Using Deep Learning Approaches in Medical Imaging
Data Privacy
Concerns:
• Sensitive
Information
• Regulatory
Compliance
Data
Centralization
Issues:
• Data Security
Risks
• Infrastructure
Costs
Limited Data
Access:
• Data Silos
• Collaboration
Barriers
Bias and
Generalizatio
n:
• Limited Diversity
• Sample Size
Limitations
High
Computation
al Costs:
• Resource
Intensive
• Scalability Issues
Model
Updates and
Maintenance:
• Frequent
Updates Needed
• Coordination
Challenges

4. Benefits of using Federated Learning for medical
imaging
Data Privacy
Devices don't need to share their raw data with each other or a central server.
Data Security
It reduces the risk of data breaches or unauthorized access to sensitive information.
Scalability
It can effectively train models on massive datasets distributed across numerous devices.
Flexibility
It allows models to be trained on data from different sources and domains.

5. LITERATURE SURVEY
SL NO. TITLE YEAR OF
PUBLICATION
JOURNEL AUTHOR TOPIC
1 Federated Learning for
Healthcare Informatics
2020 IEEE
Transactions
on Artificial
Intelligence
Andrew R.
Willis,
Juncheng Li,
Tianlong
Chen,
Zhangyang
Wang
The paper discusses how federated
learning can be applied to healthcare
data to improve machine learning
models while maintaining patient
privacy. It covers methodologies, case
studies, and potential challenges.
2 Federated Learning in
Medical Imaging: A Survey
2021 Medical
Image
Analysis
Daniel
Rueckert, Jo
Schlemper,
Christian
Baumgartner
This survey explores the applications
of federated learning in medical
imaging, including segmentation,
classification, and prediction tasks. It
reviews existing literature, highlights
the benefits of federated approaches,
and discusses the future directions of
this field.

6. SL NO. TITLE YEAR OF
PUBLICATION
JOURNEL AUTHOR TOPIC
3 Privacy-Preserving
Machine Learning for
Medical Image
Analysis
2022 Nature
Machine
Intelligence
He He,
Murali
Annavaram,
Salman
Avestimehr
The article examines how federated
learning can be used to perform machine
learning on medical images while
preserving patient privacy. It presents
various techniques for secure data sharing
and processing in federated learning
environments.
4 Federated Learning
for Precision
Medicine: Challenges
and Opportunities
2021 Journal of the
American
Medical
Informatics
Association
(JAMIA)
Yang Liu,
Qiang Yang,
Yin Zhang
This paper addresses the application of
federated learning in precision medicine,
emphasizing the opportunities it presents
for collaborative research and
personalized healthcare while protecting
patient data privacy.
5 Enabling
Collaborative
Learning in
Healthcare Using
Blockchain-Based
Federated Learning
2022 IEEE Journal
of Biomedical
and Health
Informatics
Xuebin Ren,
Jie Xu,
Jianyong
Wang
The study explores the integration of
blockchain technology with federated
learning to enhance data security and
integrity in collaborative healthcare
research. It provides a framework for
implementing such a system and discusses

7. Federated Learning Algorithms
Federated Averaging
This is a basic algorithm where
devices compute model
updates locally and then send
them to a central server to be
averaged.
Federated Stochastic
Gradient Descent
This algorithm uses stochastic
gradient descent to update
models iteratively, but with
decentralized data. It's similar
to Federated Averaging, but
with additional features for
efficiency and performance.
Federated Proximal
This algorithm is used for
constrained optimization
problems, ensuring that the
model updates remain within a
defined constraint.

8. Federated Averaging
1 Local Training
Each device trains a model on its own data.
2 Model Aggregation
A central server collects the model updates
from all devices.
3 Global Model Update
The server averages the model updates and
distributes the new model to the devices.

9. Federated Stochastic Gradient
Descent
1 Stochastic Updates
Each device uses stochastic gradient descent to update the model, using only a
subset of its data.
2 Noise Reduction
The algorithm introduces techniques like momentum and adaptive learning
rates to reduce the noise in the stochastic updates.
3 Communication Efficiency
It optimizes communication by sending smaller updates to the server.
4 Convergence
The algorithm aims to converge to a globally optimal model, even with
decentralized data.

10. Federated Proximal
Problem Setup
The problem involves minimizing a loss function while satisfying certain
constraints.
Local Updates
Each device computes a local update that minimizes the loss function, subject
to its local constraints.
Server Aggregation
The server aggregates the local updates and applies a projection operator to
ensure that the global model satisfies the overall constraints.
Model Distribution
The server distributes the updated model to all devices for further local
training.

11. Applications of Federated Learning
Healthcare Train models for disease prediction, personalized
medicine, and medical imaging.
Mobile Devices Improve device performance, personalize user
experiences, and detect fraud.
Finance Develop fraud detection systems, risk assessment
models, and personalized financial advice.
Internet of Things Optimize resource allocation, predict equipment
failures, and enhance user experiences.

12. Federated Learning Frameworks
TensorFlow Federated
A framework designed for federated learning,
providing tools for model development, training, and
evaluation.
Flower
An open-source framework for decentralized machine
learning, with a focus on flexibility and customization.

13. TensorFlow Federated
Codebase
Provides a high-level API for creating and training federated learning models.
Scalability
Handles large-scale federated learning scenarios with numerous devices and datasets.
Visualization
Provides tools for visualizing the training process and model performance.
Analysis
Offers mechanisms for analyzing the training data and evaluating the model's performance.

14. Flower
Flexibility
Supports various federated learning strategies and custom algorithms.
Customization
Allows for modifications and extensions to meet specific needs.
Open Source
It's an open-source framework, allowing for community contributions and collaboration.
Documentation
Provides comprehensive documentation and tutorials to guide users.

15. Conclusion and Future Directions
Federated learning is a transformative technology with the potential to revolutionize
machine learning while addressing data privacy concerns. As research and development
continue, we can expect to see even more innovative applications and advancements in
this exciting field.
Conclusion

16. Future Directions
1. Advancements in Federated Learning
o Improved algorithms for better model aggregation
o Enhanced security measures (e.g., differential privacy)
2. Integration with Other Technologies
o Combining with edge computing and blockchain
3. Broader Adoption
o Wider use in various medical fields