NithikshaPatelGSAndSagarikaKgGowdaddd.ppt

federated Learning

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. Existing system and limitations. Aim of project. Proposing system along with advantages. Future Enhancement. Software requirement specification. Algorithm. Flowchart. Screenshots. 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 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 deep learning 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.

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

5. 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.

6. 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. Traditional master worker type of distributed learning assumes a trusted central server and focuses on the privacy issues of the linear learning models.

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

8. FUTURE ENHANCEMENT Using deep learning framework to develop a serverless private deep learning models. Design a system with mixed approach of deep learning and secret sharing. 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) t mux : 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 7 th 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 [ 3 ] https//:ai.googleblog.com/2017/04//federated-learning-collaborative.html

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