To design a federated learning system where all data processing and model learning occur on-device without any data being transmitted off-device -- 2

Please bid only if you are ready to work in that price my budget is no more than 100 euros.

Designing Lightweight Models:

Task List:

Develop small-footprint neural networks tailored for on-device learning. This includes designing network architectures with fewer parameters such as MobileNets, EfficientNets, or TinyML models.

Use techniques like knowledge distillation to transfer learning from large, sophisticated models to smaller, more efficient ones that can run on-device.

Implement model pruning and quantization techniques to reduce the computational cost and size of models while maintaining acceptable performance levels.

On-device Data Processing:

Task List:

Design data preprocessing pipelines that can efficiently handle and transform raw user data (like browsing history and app usage) into a feature set that the model can interpret.

Create algorithms for on-device keyword extraction from text data, leveraging lightweight NLP methods. This might involve the use of pre-trained embeddings that can be fine-tuned locally.

Explore the use of databases and storage methods that are efficient and have a small disk footprint for maintaining user profiles locally.

Resource Management:

Task List:

Profile the computational resources required for training and inference to understand the model's performance on different hardware.

Implement dynamic model scaling techniques where the complexity of on-device models adjusts based on the available computational resources.

Research and integrate methods for efficient memory management, ensuring the system uses the least amount of RAM possible.

User Profile Creation:

Task List:

Develop an algorithm that can create and update a user profile based on extracted keywords and inferred interests without transmitting data off the device.

Use local storage solutions to securely store these profiles, ensuring that they are only accessible by the authorized application processes.

Implement mechanisms for profile aging and pruning to remove outdated information and keep the profile relevant.

Energy Efficiency:

Task List:

Optimize algorithmic efficiency to ensure that the training and inference processes consume minimal battery life.

Schedule intensive computational tasks for times when the device is charging or otherwise idle.

Integrate with device APIs to gain insights into power consumption and adjust the computational load accordingly.

Software Development Kit (SDK) for Integration:

Task List:

Create an SDK for developers that encapsulates all the above features, allowing easy integration of the federated learning system into various apps.

Ensure the SDK is compatible with multiple platforms and devices, particularly focusing on low-end devices.

Document the SDK extensively to provide clear guidelines on how to integrate it with existing app ecosystems.

Evaluation and Testing:

Task List:

Establish comprehensive benchmarks to measure the performance, accuracy, and resource usage of the system across a range of devices.

Python Machine Learning (ML) Perlombongan Data Web Scraping Flutter

ID Projek: #37420598

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