federated learning method of machine learning

BACHELOR OF TECHNOLOGY
IN
COMPUTER SCIENCE AND ENGINEERING
2020-2024
FEDERATED LEARNING
PRESENTED BY
PUNYAPU HARSHITHA
20NG1A0548
CSE-A
Under the Guidance of
Dr. K P N V SATYA SREE
Professor
19-04-2024 URCET-CSE dept 1

CONTENTS
Introduction
Why Federated learning?
How Federated Learning Works?
Model Aggregation techniques
Benefits And Challenges
Use Cases
Implementation
Future Trends And Conclusion

Introduction
What is Federated Learning?
• Federated Learning trains AI models using data from different devices without
moving that data to a central server.
• Each device learns on its own data and shares only what it learns, keeping
personal data safe.

• This approach protects
user privacy and allows
for training on sensitive
or distributed data.

How Does It Works?
• Start: A central server creates a
basic model.
• Selection: It picks some devices
from a network.
• Sharing: The model is sent to these
devices.
• Local Learning: Each device
improves the model using its own
data, without sharing the data itself.

• Updates: Devices send their improvements back to the server.
• Combining: The server merges these improvements into a new model.
• Repetition: Steps 3 to 6 are repeated for several rounds.
• Improvement: With each round, the model gets better by learning from more
devices.
• Final Model: Eventually, a final improved model is achieved.
• Use: This model can be used for making predictions or classifications without
exposing individual data.

Importance of Federated Learning in
Privacy-Preserving AI
• Federated Learning is crucial for keeping personal data safe in AI.
• It prevents big data breaches by spreading training process across devices.
• It helps companies follow privacy rules by training AI models directly on
users' devices.

Why Federated Learning?
Challenges with Centralized Learning Models
1.Privacy Worries: Centralized models gather all data in one place, raising
privacy concerns.
2.Limited Data Access: Centralized models struggle to access diverse
datasets from different sources.
3.Network Issues: Transmitting large data volumes strains networks and
slows down processes.

Evolution towards Decentralized Approaches
• People want more control over their data.
• More processing is happening on device such as smartphones, IoT
devices, and edge servers , making Federated Learning a better fit.
• Federated Learning allows multiple parties to train models together without
sharing their raw data.

Standard Approach VS Federated Learning

How Federated Learning Works?

Model Aggregation Techniques
1. Federated Averaging:
• Each device sends its model updates.
• The updates are averaged to create a better global model.
2.Federated Stochastic Gradient Descent (FedSGD):
• Devices send gradients (directions to improve the model).
• The global model adjusts based on these gradients.
3.Secure Aggregation:
• Ensures updates are combined securely.
• Protects individual data while improving the global model.

Benefits
• Enhanced Privacy
Federated Learning keeps your data safe on your device, reducing the risk
of privacy breaches.
• Faster Processing
By training models directly on devices, Federated Learning speeds up data
processing, especially for real-time tasks.

Challenges
Communication Overhead: Federated
Learning can sometimes slow down due to
communication between devices and
servers.
Data Differences: Devices may have
different types or amounts of data, making
it tricky to combine their updates
seamlessly.

Use Cases
• Healthcare
• Telecommunications
• Finance
• Smart Grid Optimization
• Manufacturing and Industry 4.0
• Autonomous Vehicles
• Agriculture and Precision Farming

Example
Healthcare: Predictive Models without Sharing Sensitive Data
Scenario:
Healthcare institutions collaborate to predict diseases or patient outcomes.
Challenge:
Patient data must stay private due to regulations.
Solution:
Use Federated Learning to train models on local data. Only share model
updates.

Benefits:
Keeps patient data private, aids collaboration, and builds accurate models
securely.

Implementation
Choosing Frameworks
• Select frameworks like TensorFlow Federated or PySyft for
implementing Federated Learning.
TensorFlow Federated (TFF)
Developed by Google helps define Federated Learning tasks and manage
communication.
PySyft
Built on PyTorch, it ensures privacy in computations using techniques
like differential privacy

• These tools provide the necessary resources to manage training across
devices.
Scaling Up:
1.Ensure scalability by optimizing communication and aggregation
processes.
2.Balancing loads, managing resources, and maintaining reliability are
vital for efficient operation.

Future Trends
1.Advanced Algorithms: Expect smarter ways to train models.
2.Edge Computing Integration: Devices will help process data faster.
3.Expanded Applications: More places will use Federated Learning for
different tasks.
4.Privacy Improvements: More ways to protect your data.

Conclusion
• Federated Learning makes machine learning collaborative and secure.
• With better algorithms and privacy protection, it's set for growth.
• Businesses should adopt it for efficient AI while protecting privacy.
• As it becomes standard, Federated Learning will transform many areas with
personalized solutions.

THANK YOU

federated learning method of machine learning

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