The document presents a seminar on federated learning, detailing its approach to machine learning that allows multiple devices to collaboratively train models without sharing raw data. It discusses the objectives, advantages, proposed systems, and future enhancements of federated learning, emphasizing privacy and security. The conclusion highlights federated learning as a transformative method that prioritizes decentralized training and privacy in model development.

1. PES Institute of Technology and Management, Shivamogga
Department of Master of Computer Applications
Seminar On
“FEDERATED LEARNING BASED ON DEEP LEARNING”
Presented by
NITHIKSHA PATEL G S 4PM23MC028
RAKSHITHA G 4PM23MC032
SAGARIKA K G 4PM23MC035
Under the Guidance of
Mr. MUSHEER AHMED
Asst. Professor
Dept. of MCA
PESITM, Shivamogga

2. TABLE OF CONTENT
Introduction.
Scope and objectives.
Aim of project.
Existing system and limitations.
Proposing system along with advantages and disadvantages.
Future Enhancement
Software requirement specification.
Algorithm.
Flowchart.
Conclusion.
Reference.

3. INTRODUCTION
Federated learning is a machine learning approach that allows multiple devices
or systems to collaboratively train a machine learning model without the
need to share their raw data with each other. In traditional centralized
machine learning approaches, data is collected and aggregated into a central
location before a model is trained.
Federated learning is a training the model on client devices using the federated
averaging algorithm is shown to perform better than server based training using
iterative algorithm. The algorithm is used on the server to combine updates from
the clients and produce a new global model. We explore a secure decentralized
learning model using neural networks.

4. SCOPE AND OBJECTIVES
SCOPE
• Access to heterogeneous data: Federated learning guarantees access to data
spread across multiple devices, locations and organizations.
• Federated learning’s applications are spread over a number of industries including
defense, telecommunications, IOT and pharmaceutics.
OBJECTIVES
• Implement a secure decentralized leaning model using neural networks and
developing a globally shared model to where data is and having train models for
each users.
• We explore the Machine learning techniques to propose a privacy-preserving and

5. EXISTING SYSTEM
• Federated learning has been proposed to allow collaborative learning
of deep learning model among multiple parties where each party can
keep its data private.
Limitations
•The next generation of artificial intelligence is built upon the core idea
revolving around data privacy.

6. AIM OF PROJECT
Federated learning aims at training a machine learning
algorithm , for instance deep neural networks , on multiple local
datasets contained in local nodes without explicitly exchanging
data samples.

7. Advantages
 Communication overhead
 Privacy preservation and data protection
 Security and robustness
PROPOSED SYSTEM
We develop differential privacy based schemes to protect each party’s rate of privacy
and integrity and propose an nearest aggregation algorithm to protect the system
from potential attacks.

8. FUTURE ENHANCEMENT
1. Using deep learning framework to develop a serverless private deep
learning models.
2. Design a system with mixed approach of deep learning and secret sharing.
3. Scaling and designing of the distributed systems for a large number of
clients.

9. SOFTWARE REQUIREMENT SPECIFICATION
Software Requirements
Python: version 2.1 or above (recommended 3.3 and stable)
tmux : allows multiple terminal sessions to be accessed simultaneously in a
single window.
 OS: Microsoft Windows 8/10, Mac pr Ubuntu 18 or higher.
Hardware Requirements
 Intel i5 7th
generation or higher
Memory: minimum 8GB of ram and 500GB Disk space

10. ALGORITHM
Whenever the user enters some information, the
following step takes place:
• Step 1: The particular device will download the
current model.
• Step 2: The model would make improvements from
the new data that we got from the device.
• Step 3: The model changes are summarized as an
update and communicated to the cloud. This
communication is encrypted.
• Step 4: On the cloud, there are many updates coming
in from multiple users. These all updates are
aggregated and the final model is built.

11. FLOWCHART

12. SCREENSHOTS

13. CONCLUSION
In conclusion federated learning represents a transformative approach to machine
learning that prioritizes privacy and decentralized model training.
Machine learning programs are typically less resource-intensive and can run on
conventional computers. Deep learning models require more computational power
due to the complexity of artificial neural network and the large volume of data they
process

14. REFERENCES
[1] Akihito Taya ,Takayuki Nishio , Masahiro Morikura , koji Yamamoto
“Decentralized and Model- Free Federated Learning: Consensus - Based
Distillation Function in Function space proceedings of the IEEE, vol, 86,, no.
11,pp,2278-2324,January 2020.
[2] Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, James Joshi,
Heilo Ludwig, “FedV : Privacy- Preserving Federated Learning over
Vertically Partitioned Data “, IEEE, March 2921
[4] https//:ai.googleblog.com/2017/04//federated-learning-
collaborative.html

15. THANK YOU