federated learning: Collabarative learning

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Federated Learning
By
Shivanand Sahu Roll no:A123007
2023-25

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 Introduction
 How Federated Learning Works
 Core challenges
 Advantages
 Application
 Recent Development
 Conclusion
 References
TOPICS TO BE DISCUSSED

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National
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logy
 Federated learning is a machine learning
technique that trains an algorithm across
multiple decentralized edge devices
or servers holding local data samples,
without exchanging them.
It enables multiple entities to
collaboratively train a model while ensuring
that their data remains decentralized.
INTRODUCTION

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The server has an untrained model
How Federated Learning Works

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Server sends a copy of that model to the nodes

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The nodes now also have the untrained model

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The nodes have data on which to train their model

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Each node trains the model to fit the data they have.
Each node sends a copy of its trained model back to the server, and the server
combines these model by taking average and it an iterative process.

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CORE CHALLENGES
Expensive Communication.
Systems Heterogeneity: Diversity of devices participating in the training
process can vary in computational power, network connectivity, battery life.
Statistical Heterogeneity: situation where devices participating in the training
process have local datasets that follow different distribution.

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Ensuring privacy, since the data remains on the user’s device.
Lower latency, because the updated model can be used to make
predictions on the user’s device.
Smarter models, given the collaborative training process.
Less power consumption, as models are trained on a user’s device.
Advantages

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Application
 Healthcare Industry: To train models using
distributed patient data while maintaining
privacy.
Financial Sector: It allows financial firms to
collaborate without disclosing confidential
customer data.
Autonomous Vehicles: It provide a better
and safer self-driving car experience with
real-time data and predictions.

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RECENT DEVELOPMENTS IN FL
 One-shot federated Learning: It’s a variation of federated learning that aims to
achieve model training in a single round of communication
 Incentive Mechanisms: address the challenges of ensuring device
participation by motivating them to contribute their resources to the training
process.
 Blockchain in FL :development in security and privacy.

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Conclusion
Federated learning makes it easier, safer, and cheaper to apply machine
learning in the world’s most regulated, competitive, and profitable industries.
 It’s also an area of very active current research, with open problems in privacy,
security, personalization, and other areas.

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References
[1] Li, Tian, et al. "Federated learning: Challenges, methods, and future directions." IEEE
signal processing magazine 37.3 (2020): 50-60.
[2] Mammen, Priyanka Mary. "Federated learning: Opportunities and challenges." arXiv
preprint arXiv:2101.05428 (2021).
[3] https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
[4] https://en.wikipedia.org/wiki/Federated_learning
[5] https://arxiv.org/abs/2104.11375

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